A New Law to Describe Quantum Computing’s Rise? (If true, everything changes…)
That rapid improvement has led to what’s being called “Neven’s law,” a new kind of rule to describe how quickly quantum computers are gaining on classical ones. The rule began as an in-house observation before Neven mentioned it in May at the Google Quantum Spring Symposium. There, he said that quantum computers are gaining computational power relative to classical ones at a “doubly exponential” rate — a staggeringly fast clip.
With double exponential growth, “it looks like nothing is happening, nothing is happening, and then whoops, suddenly you’re in a different world,” Neven said. “That’s what we’re experiencing here.”
Mind-reading and Computer-brain interface
Technology that harnesses brain activity to produce synthesised speech may benefit individuals who have been robbed of the ability to talk due to a stroke or other medical conditions, researchers claim.
Known as a ‘brain decoder’, the technology is said to read people’s minds and turn thoughts into speech – a tool which could one day help doctors communicate with patients who cannot talk.
Scientists at the University of California, San Francisco (UCSF) implanted electrodes into the brain of volunteers and then decoded signals in cerebral speech centres to guide a computer-simulated version of their vocal tract – lips, jaw, tongue and larynx – to generate speech through a synthesiser.
The results from the volunteers were mostly intelligible, although the researchers have noted that the speech is somewhat slurred in parts.
“We were shocked when we first heard the results – we couldn’t believe our ears,” said UCSF doctoral student Josh Chartier. “I was incredibly exciting that a lot of aspects of real speech were present in the output from the synthesiser.”
Results from the study have raised hope among the researchers that, with improvements, a clinically viable device could be developed for patients with speech loss in the years to come.
“Clearly, there is more work to get this to be more natural and intelligible,” Chartier added, “but we were very impressed by how much can be decoded from brain activity.”
A stroke, ailments such as cerebral palsy, amyotrophic lateral sclerosis (ALS), Parkinson’s disease and multiple sclerosis, brain injuries and cancer sometimes take away a person’s ability to speak.
Such conditions result in some people using devices that track eye or residual facial muscle movements to spell out words letter-by-letter. These methods, however, are slow, delivering typically no more than 10 words per minute in comparison to 100-150 words per minute in natural speech.
The five volunteers who took part in the study were all epilepsy patients. Although they were all capable of speaking, they were given the opportunity to participate as they were already scheduled to have electrodes temporarily implanted in their brains to map the source of their seizures before neurosurgery. Future studies will test the technology on people who are unable to speak.
The volunteers read aloud while activity in brain regions involved in language production was tracked. The researchers discerned the vocal tract movements needed to produce the speech and created a “virtual vocal tract” for each participant that could be controlled by their brain activity and produce synthesised speech.
“Very few of us have any real idea, actually, of what’s going on in our mouth when we speak,” said neurosurgeon Edward Chang. “The brain translates those thoughts of what you want to say into movements of the vocal tract, and that’s what we’re trying to decode.”
The researchers found that during the study, they were more successful in synthesising slower speech sounds such as “sh” and less successful with the abrupt sounds such as “b” and “p”.
Furthermore, the technology did not work as well when the researchers tried to decode the brain activity directly into speech, without using a virtual vocal tract.
“We are still working on making the synthesised speech crisper and less slurred,” Chartier said. “This is in part a consequence of the algorithms we are using, and we think we should be able to get better results as we improve the technology.”
“We hope that these findings give hope to people with conditions that prevent them from expressing themselves that one day we will be able to restore the ability to communicate, which is such a fundamental part of who we are as humans,” he added.
The study has been published in the journal Nature.
Solving Elon Musk
To be fair, Elon isn’t a problem to be solved. He is just doing what he does –geeking out, innovating, living life. But as a fan of innovators and a student of markets, I can’t help but try to understand the motives of Mr. Musk. (Full disclosure: I’m a Elon Musk fan)
So, to speak presumptively, Elon probably has something close to a technological messiah complex. This isn’t bad, quite the contrary. We need brilliant people to care about the future. I agree with Tim Urban’s take on Elon. I’ll summarize: Start with a good outcome for humanity and work backwards. Why do I bring this up? Okay, I’ll jump to the big question:
Why did Elon buy Solar City?
Buying Solar City wrecked the balance sheet of Tesla, and placed the company in a much more difficult place. Of all the companies that Elon is involved in Solar City is the only one with no obvious technological edge. In fact, if Tesla wanted to be a one-stop-shop for all things carbon-free it would make more sense to simply partner/license the solar tech. Why buy Solar City?
The solar-tile? Me thinks not. Not enough innovation there.
To bail out cousins? Surely not, there are much more efficient ways to help family than to take over a struggling company.
To facilitate collaborative engineering between Tesla and Solar City? Maybe. This is supposed to be the year of solar-tile and the power-wall. But buying the company was unnecessary. Just do a deal with Solar City, but develop the power-wall as compatible with all solar units. Be Microsoft not Apple. And don’t take over a bunch of debt.
I believe that Mr. Musk was somehow thinking as a technological messiah, not a capitalist, when he did the Solar City deal. Now his shareholders are paying the price, and the risk-of-ruin for Tesla is substantially higher.
Whatever the case, this is the year where Solar City either makes sense, or doesn’t.
Algo at work
Part of this blog’s goal is to show our actual numbers in a more accountable way. Here are the signals sent by our current US market algo. These signals are drawn from meta-data derived from the whole market. Currently, we are struggling to find trade-able meta-data for any one stock. We have had some success looking for that data in sectors (and we may post a riskboard for sectors in the future.) However, the meta-data for whole markets is excellent.
Compound interest and exponential thinking
Our minds do not grasp non-linear math, not easily anyway. This is why young people don’t save money and why older people shrug off technological change. This is the best illustration I have seen on this subject.
This understanding applies directly to our world, both socially, scientifically, and financially. The folks over at Ark Invest gives us this list (their work is fantastic).
1. Deep Learning –
Is it a larger opportunity than the Internet?
2. Digital Wallets –
Could they spell the end of traditional banks?
3. Cryptocurrencies –
Are we witnessing the rise of an alternative financial system?
4. Battery Cost Tipping Point –
Could EVs become cheaper than comparable gas-powered cars?
5. Autonomous Taxi Networks –
Will they become the most valuable investment opportunity in public equity markets?
6. Next Generation DNA Sequencing –
Could it unlock the code to life, disease, and death?
7. CRISPR For Human Therapeutics –
Will health care become cheaper and curative?
8. Collaborative Robots –
Will robots be your next co-worker?
9. 3D Printing for End-Use Parts –
Will manufacturing ever be the same?
Why Invest in China?
I was recently asked about China. People talk about the debt problem, the demographic problem, the manipulated data problem, the investor-rights problem, the human-rights problem, etc. After all these problems, why invest?
Because China is the biggest thing happening in the world economy. It’s impossible to ignore. It would be like ignoring the US economy from 1800-2000. As flawed as China is, we must engage, gather data, define frameworks, code algorithms. China will not be our largest position for as far as the eye can currently see. Too many problems. But we must engage.
One can currently engage through SGX (Singapore) and HKSE (Hong Kong) futures, while avoiding much of the specific problems of being a stock owner.
Climbing the Worry Wall
Nothing about this bull market makes sense to me. However, because I do not argue with the market, nor do I pretend to understand things I do not actually understand –I have participated in it. Ultimately, this is the genius of algorithmic trading systems. Every bull and bear market happens at the margins of the largest market-thesis. And it is debated everyday at the bid and the ask. Financial commentators will say this matters, that matters, look at this historical comparison, look at this valuation, etc. The market does not care. Find data that others are not talking about. Then obsess over it until you find the subtle asymmetrical insight that resides inside it. That’s the honeypot at the end of the rainbow.
Public vs private valuation
There is an interesting imbalance in the public and private markets. Private capital will pay substantially more for an inferior company—and hold it longer. Cathie Wood at Ark Invest has pointed this out repeatedly. Listen to her interview with Azeem Azhar. Analysts have become very backward looking in their search to understand valuation. Because, perceiving a different reality in the present (versus the past) is tantamount to saying “it’s different this time.” Let’s postulate a few truths:
- Innovation is happening across more platforms than anytime in history
- Platform level changes (i.e. electricity, transportation) change everything they touch
- We live in a world of excess capital and surging numbers of knowledge workers (more money and more minds to solve problems than ever)
- The vast majority of public money is trapped in capitalistic dead-ends (i.e. funding zombie governments)
- Valuation is always understood backwards, while capital must be invested forwards
- Private markets are closed to the non-wealthy. Public market become the only option for most people.
- But the wealthy are just as vulnerable to herd-behavior is anyone.
How do we profit from the herd-behavior in private equity (that is rightly seeing the stunning changes coming across broad technology platforms and therefore paying wild valuations for inferior companies) while remaining agile and liquid?
We here at ThinkGlobalMacro are doing it by focusing on the indexes that capture that change (Nasdaq and similar) while avoiding or hedging with the indexes that reflect the past (DJIA). Then, we create health monitoring algorithms (that look back) to define the health-zones of the index, the economy is which it is placed, and the government structures that regulate it.
Risk Change on Japan
So our risk system for Japan signaled a Risk Off overnight. Since we focus on ETFs and derivatives it is simple to scale-down exposure. Trading single stocks makes it difficult.
The most informative AGI lecture ever
This is a deep dive into Artificial General Intelligence. But it is fantastic. Highly recommend. https://www.infoq.com/presentations/general-ai-ml