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age of machine learning

This is a quite interesting article. And I agree that we are entering the age of machine learning. However the argument is structured as though the other ages have ended. They most certainly have not. Indeed much of humanity is still in the age of faith. And this is not an altogether bad thing . We are still in the industrial age … just in certain sectors of the economy. Technology age is not gone but still grinding ahead. History is ultimately a layer cake … it is just on top of this large cake we now add machine learning. This is very important when valuing companies. When a company gets a value or multiple assigned to it from one age it is almost impossible to move the valuation forward. Thanks for example of IBM. 

https://venturebeat.com/2017/06/25/the-information-age-is-over-welcome-to-the-machine-learning-age/

I first used a computer to do real work in 1985.

I was in college in the Twin Cities, and I remember using the DOS version of Word and later upgrading to the first version of Windows. People used to scoff at the massive gray machines in the computer lab, but secretly they suspected something was happening.

It was. You could say the information age started in 1965 when Gordon Moore invented Moore’s Law (a prediction about how transistors would double every year, later changed to every 18 months). It was all about computing power escalation, and he was right about the coming revolution. Some would argue the information age started long before then, when electricity replaced steam power. Or maybe it was when the library system in the U.S. started to expand in the 30s.

Who knows? My theory is it started when everyone had access to information on a personal computer. That was essentially what happened for me around 1985 — and a bit before that in high school. (Insert your own theory here about the Apple II ushering in the information age in 1977. I’d argue that was a little too much of a hobbyist machine.)

We can agree on one thing. We know that information is everywhere. That’s a given. Now, prepare for another shift.

In their book Machine, Platform, Crowd: Harnessing Our Digital Future, economic gurus Andrew McAfee and Erik Brynjolfsson suggest that we’re now in the “machine learning” age. They point to another momentous occasion that might be as significant as Moore’s Law. In March of last year, an AI finally beat a world champion player in Go, winning three out of four games.

Of course, pinpointing the start of the machine learning age is difficult. Beating Go was a milestone, but my adult kids have been relying on GPS in their phones for years. They don’t know how to read normal maps, and if they didn’t have a phone, they would get lost. They are already relying on a “machine” that essentially replaces human reasoning. I haven’t looked up showtimes for a movie theater in a browser for several years now. I leave that to Siri on my iPhone. I’ve been using an Amazon Echo speaker to control the thermostat in my home since 2015.

In their book, McAfee and Brynjolfsson make an interesting point about this radical shift. For anyone working in the field of artificial intelligence, we know that this will be a crowdsourced endeavor. It’s more than creating an account on Kickstarter. AI comes alive when it has access to the data generated by thousands or millions of users. The more data it has the better it will be. To beat the Go champion, Google DeepMind used a database of actual human-to-human games. AI cannot exist without crowdsourced data. We see this with chatbots and voicebots. The best bots know how to adapt to the user, how to use previous discussions as the basis for improved AI.

Even the term “machine learning” has crowdsourcing implications. The machine learns from the crowd, typically by gathering data. We are currently seeing this play out more vibrantly with autonomous cars than any other machine learning paradigm. Cars analyze thousands of data points using sensors that watch how people drive on the road. A Tesla Model S is constantly crowdsourcing. Now that GM is testing the self-driving Bolt on real roads, it’s clear the entire project is a way to make sure the cars understand all of the real-world variables.

The irony here? The machine age is still human-powered. In the book, the authors explain why the transition from steam power to electric power took a long time. People scoffed at the idea of using electric motors in place of a complex system of gears and pulleys. Not everyone was on board. Not everyone saw the value. As we experiment with AI, test and retest the algorithms, and deploy bots into the home and workplace, it’s important to always keep in mind that the machines will only improve as the crowdsourced data improves.

We’re still in full control. For now.

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Robo-economist… not kidding

http://www.belfasttelegraph.co.uk/business/news/pwc-predicts-roboeconomist-could-make-firm-most-accurate-forecaster-on-market-35863155.html

PwC is on the cusp of launching a robo-economist that could make the company the “most accurate” economic forecaster on the market.

The professional services firm has developed a form of artificial intelligence (AI) with a 92% strike rate when it comes to predicting the result of UK gross domestic product (GDP).

It discovered the AI’s “incredible accuracy” after testing to see if the machine could pinpoint historic GDP results without knowing the outcome.

But while Jonathan Gillham, PwC’s director of economics, joked that the AI had already started to supersede his job, the firm said there were no plans to replace staff with automation and the program would work alongside human economists.

He said: ” We have been using an AI technique to forecast the UK economy and we will be launching that (…) in July.

“Each quarter, the Office for National Statistics publishes its estimate for GDP and we have been able to use an AI technology base to get that right 92% of the time for the last five years.

“We pretended five years ago what we would have forecast if we didn’t know the number, and the machine learning process that we have developed has predicted it with incredible accuracy.

“Whether that will hold up in July when we launch it, who knows, (…) but if it’s correct than it would make us the most accurate forecaster on the market.”

PwC said the programme still needed “input and judgement” from human economists to identify problems, select the best models and interpret the results.

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The weekly trade log

Current position is long the nassies, gold, and 10y bonds… we may switch some of the portfolio to /es if that strength continues… Also the gold position is really a hedge, but if real rates keep edging up it should make a dollar…

We continue to follow momentum but expect a summer scare… we have no theories on where it will start. We can rely on the Market to tell us –it can’t keep a secret. China has an inverted yield curve, so we expect drama there within a year.  Also, we expect serious US sell off next year. Maybe down 15-20% But we will follow the market action, not the certainties (lol) of mortals…

Below you can see the summation strength edging over into SPX, but everything is weakening. If we don’t bounce soon, it is probably a short-term top and we’ll have our summer scare quickly…

Beta-rotation actually goes back to stocks, giving utilities a break…

Total option ratio still likes long /nq

Now for some momentum comparisons. The Asia model likes Indonesia and Taiwan, with Singapore dragging (might get some reversion there). Note all of these countries have futures contracts (mostly through Singapore exchange). I am not currently trading the Asia algos, because of (1) unknown risks with the Chinese (and Indian) yield structure and (2) our current desire to lower leverage.

Sector momentum. Note the health surge. Tech is second place now. I feel sorry for my friends who bought energy last year. Painful…

Some etfs that track the companies chasing the future… Look at biotech jump…

But in these macro categories /nq remains the place to sit..

And in the continuing AI/everything race… Its Tesla, Nvidia, and Alibaba…

If you have any sense you don’t bet all your $. I take profits out every year and put in what I call slow money. It’s low risk, slow money. For me that means cheap Vanguard index funds. This is my slow money universe here.

I’m in San Diego/Orange county all next week, working, and training bjj with some Atos killers. Shoot me a message if you wanna have lunch. It might work out, you never know. Running private money can be a lonely business.

Godspeed and happy trading…

 

 

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AI checkup…

This conference adds much needed perspective on the AI revolution and the current state of the art. Indeed we are many breakthroughs away from artificial general intelligence.  The current level of technology does not quite justify the current level of hype. However my interest in AI is related to the fact that of all the current narratives that can drive investors to want to put their money in a stock, AI and it’s many sub-fields (such as transportation as a service, big data, etc) it’s by far the most beguiling story. 

https://venturebeat.com/2017/06/23/ai-is-still-several-breakthroughs-away-from-reality/

While the growth of deep neural networks has helped propel the field of machine learning to new heights, there’s still a long road ahead when it comes to creating artificial intelligence. That’s the message from a panel of leading machine learning and AI experts who spoke at the Association for Computing Machinery’s Turing Award Celebration conference in San Francisco today.

We’re still a long way off from human-level AI, according to Michael I. Jordan, a professor of computer science at the University of California, Berkeley. He said that applications using neural nets are essentially faking true intelligence but that their current state allows for interesting development.

“Some of these domains where we’re faking intelligence with neural nets, we’re faking it well enough that you can build a company around it,” Jordan said. “So that’s interesting, but somehow not intellectually satisfying.”

Those comments come at a time of increased hype for deep learning and artificial intelligence in general, driven by interest from major technology companies like Google, Facebook, Microsoft, and Amazon.

Fei-Fei Li, who works as the chief scientist for Google Cloud, said that she sees this as “the end of the beginning” for AI, but says there are still plenty of hurdles ahead. She identified several key areas where current systems fall short, including a lack of contextual reasoning, a lack of contextual awareness of their environment, and a lack of integrated understanding and learning.

“This kind of euphoria of AI has taken over, and [the idea that] we’ve solved most of the problem is not true,” she said.

One pressing issue identified by Raquel Urtasun, who leads Uber’s self-driving car efforts in Canada, is that the algorithms used today don’t model uncertainty very well, which can prove problematic.

“So they will tell you that there is a car there, for example, with 99 percent probability, and they will tell you the same thing whether they are wrong or not,” she said. “And most of the time they are right, but when they are wrong, this is a real issue for things like self-driving [cars].”

The panelists did concur that an artificial intelligence that could match a human is possible, however.

“I think we have at least half a dozen major breakthroughs to go before we get close to human-level AI,” said Stuart Russell, a professor of computer science and engineering at the University of California, Berkeley. “But there are very many very brilliant people working on it, and I am pretty sure that those breakthroughs are going to happen.”

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New Republic on conspiracies… must read…

https://newrepublic.com/article/142977/new-paranoia-trump-election-turns-democrats-conspiracy-theorists

Conspiracy theories spread like measles: First they infect the weak and vulnerable; then they spread like wildfire among the entire population. Researchers have found that if a person believes in one conspiracy, he or she is more likely to believe in others—even those unrelated to the initial theory. Which is to say, once conspiracy becomes part of our beliefs, it can be harder to see the world as it truly is. Conspiracy depends on a rejection of the world as it appears to be. Once this belief takes root, it becomes harder and harder to differentiate truth from fiction.

In other words, it is not the methodology of conspiracy that’s the problem. When paranoid thinking opens up possibilities, it can serve a useful function. The danger comes when conspiracists remain wedded to their theories in the face of conflicting information, when they refuse to do the hard work of confirming and substantiating their own assumptions and beliefs. Woodward and Bernstein did not simply point to a trail of shady campaign contributions and tweet that Nixon was behind it all. They followed the facts, step by painstaking step, all the way to the Oval Office.

The promise of conspiracy—that it will assuage our anxiety—is a false one. Watching Donald Trump from the social media sidelines, expecting at any minute that the Deep State will appear and fire a single magic bullet from the Grassy Knoll and put everything right again, is a dangerous delusion. It offers false assurance that you, as one lone individual, can’t do anything, even though American democracy has never needed you more.

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Crispr and the asymmetry of biotech investing…

The current price spurt of biotechnology leads me to think that something is brewing in the research. Biotech is a sector with tremendous asymmetric information.   The market will move before the news makes sense… this story isn’t the mover of course, just another crispr story. But something is happening. I’m watching and waiting, once more on the wrong side of the asymmetrical info…

http://Firefly Gene Illuminates Ability of Optimized CRISPR-Cpf1 to Efficiently Edit Human Genome – Scicasts https://apple.news/AVDguv3zBMy-5B3xidbUq0g

Over the last five years, the CRISPR gene editing system has revolutionized microbiology and renewed hopes that genetic engineering might eventually become a useful treatment for disease. But time has revealed the technology’s limitations. For one, gene therapy currently requires using a viral shell to serve as the delivery package for the therapeutic genetic material. The CRISPR molecule is simply too large to fit with multiple guide RNAs into the most popular and useful viral packaging system.

The new study from Farzan and colleagues helps solve this problem by letting scientists package multiple guide RNAs.

This advance could be important if gene therapy is to treat diseases such as hepatitis B, Farzan said. After infection, hepatitis B DNA sits in liver cells, slowly directing the production of new viruses, ultimately leading to liver damage, cirrhosis and even cancer. The improved CRISPR-Cpf1 system, with its ability to ‘multiplex,’ could more efficiently digest the viral DNA, before the liver is irrevocably damaged, he said.

“Efficiency is important. If you modify 25 cells in the liver, it is meaningless. But if you modify half the cells in the liver, that is powerful,” Farzan said. “There are other good cases—say muscular dystrophy—where if you can repair the gene in enough muscle cells, you can restore the muscle function.”

Two types of these molecular scissors are now being widely used for gene editing purposes: Cas9 and Cpf1. Farzan said he focused on Cpf1 because it is more precise in mammalian cells. The Cpf1 molecule they studied was sourced from two types of bacteria, Lachnospiraceae bacterium and Acidaminococus sp., whose activity has been previously studied in E. coli. A key property of these molecules is they are able to grab their guide RNAs out of a long string of such RNA; but it was not clear that it would work with RNA produced from mammalian cells. Guocai tested this idea by editing a firefly bioluminescence gene into the cell’s chromosome. The modified CRISPR-Cpf1 system worked as anticipated.

“This means we can use simpler delivery systems for directing the CRISPR effector protein plus guide RNAs,” Farzan said. “It’s going to make the CRISPR process more efficient for a variety of applications.”

Looking forward, Farzan said the Cpf1 protein needs to be more broadly understood so that its utility in delivering gene therapy vectors can be further expanded.

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Trade log update… 6/23/17

We remain long the NASDAQ futures contracts. And have begun adding hedges to the portfolio. Currently we have a gold hedge and a 10 year bond hedge buffering our equity longs.

Of course the best protection is not in portfolio construction but in derivative construction. However the prices are much higher for that kind of protection. Even with the Vix at current lows. The house (cboe)  bid/ask spread is expensive.

We are continuing to expect a drawdown this summer. And I am doubtful that the recent selloff qualifies.  I am wishing I was long the biotech index. Because there is a lot of sizzle there and we may be seeing the start of the longer push upward. But the recent advance is somewhat too violent for me to chase. Yet when I say that I wonder if I am not like every other portfolio manager who misses something and then tries to pretend they are disciplined and that’s why they missed it. Oh the catacombs of self deception…

The biggest sign of my conviction that we are entering a high-risk summer is that I am seriously considering activating a long volatility algorithm that normally sits in the gun safe (so to speak) waiting for dangerous threats.  Going long volatility is a sure way to lose money in this central bank driven environment but it is also the absolute gold standard in hedging the unknown, unseen, vague risks that may be beyond our horizons.

Also, because we are sitting on almost 50% returns over the last 12 months, one must expect to catch the attention of Mt Olympus, which will dispatch humbling events to keep the mortals from hubris…

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Quantum computing. And the art of the bubble…

Quantum computing is a element of the larger exponential technologies race that is currently driving the top large-cap technology stocks. Every bubble needs a narrative. And it is my experience that one can usually find the bubble by listening to the stories that the market tells itself. Which ever one inspires the most big eyed and breathless response is the one with a chance of blowing a bubble. Usually it is technology or bio technology because those industries are the best dream-salesmen. Remember the tech bubble? Their story was if you believe in the Internet at all you should buy the stocks because the internet is going to change the world. Currently the story that is gaining momentum is related to artificial intelligent. AI is going to change everything.  Yet the same companies can also tell the quantum computing narrative. Or the transportation revolution narrative. Or the photonic computing narrative. Or the big data narrative. Or the Internet of things narrative. Or the block chain technology narrative.   All the best stories are in those leading tech stocks. 

https://www.newscientist.com/article/2138373-google-on-track-for-quantum-computer-breakthrough-by-end-of-2017/

Alan Ho, an engineer in Google’s quantum AI lab, revealed the company’s progress at a quantum computing conference in Munich, Germany. His team is currently working with a 20-qubit system that has a “two-qubit fidelity” of 99.5 per cent – a measure of how error-prone the processor is, with a higher rating equating to fewer errors.

For quantum supremacy, Google will need to build a 49-qubit system with a two-qubit fidelity of at least 99.7 per cent. Ho is confident his team will deliver this system by the end of this year. Until now, the company’s best public effort was a 9-qubit computer built in 2015.

“Things really have moved much quicker than I would have expected,” says Simon Devitt at the RIKEN Center for Emergent Matter Science in Japan. Now that Google and other companies involved in quantum computing have mastered much of the fundamental science behind creating high-quality superconducting qubits, the big challenge facing these firms is scaling these systems and reducing their error rates.

It is important not to get carried away with numbers of qubits, says Michele Reilly, CEO at Turing Inc, a quantum start-up. It’s impossible to really harness the power of these machines in a useful way without error correction, she says – a technique that mitigates the fickle nature of quantum mechanics.

Ho says it will be 2027 before we have error-corrected quantum computers, so useful devices are still some way off. But if Google can be the first to demonstrate quantum supremacy, showing that qubits really can beat regular computers, it will be a major scientific breakthrough.

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China and the block chain…

This story is important and illustrates the advantage a slightly more totalitarian society has over a democracy in that it can quickly push innovations  rather than waiting for market forces. However this comes at the cost of a more brittle and less resilient civil society. Which depends entirely  upon the relative wisdom and judgment of a small number of leaders. Think of it as a experiment on Plato’s idea of the greatest leader being a philosopher with power. Secondarily, after the risk of poor leaders exercising bad judgment, you have the chronic problem of misallocation of capital.  This is something that Plato did not think of or understand. But the western philosopher – economist Adam Smith  perceived. The market is a much better allocator of capital than any manifestation of central planning. Nevertheless, watching the speed of China pursue any technical innovation is impressive. 

https://www.technologyreview.com/s/608088/chinas-central-bank-has-begun-cautiously-testing-a-digital-currency/

China’s central bank is testing a prototype digital currency with mock transactions between it and some of the country’s commercial banks.

Speeches and research papers from officials at the People’s Bank of China show that the bank’s strategy is to introduce the digital currency alongside China’s renminbi. But there is currently no timetable for this, and the bank seems to be proceeding cautiously.

Nonetheless the test is a significant step. It shows that China is seriously exploring the technical, logistical, and economic challenges involved in deploying digital money, something that could ultimately have broad implications for its economy and for the global financial system.

A digital fiat currency—one backed by the central bank and with the same legal status as a banknote—would lower the cost of financial transactions, thereby helping to make financial services more widely available. This could be especially significant in China, where millions of people still lack access to conventional banks. A digital currency should also be cheaper to operate, and ought to reduce fraud and counterfeiting.

Even more significantly, a digital currency would give the Chinese government greater oversight of digital transactions, which are already booming. And by making transactions more traceable, this could also help reduce corruption, which is a key government priority. Such a currency could even offer real-time economic insights, which would be enormously valuable to policymakers. And finally, it might facilitate cross-border transactions, as well as the use of the renminbi outside of China because the currency would be so easy to obtain.