Ideas for improvement – Google Translate

 

I wonder at what point Google Translate will offer the option of a hyperlink to the original text with the translation.

Sometimes the translation doesn’t make sense and getting back to the original can help – and also help one understand the other language better, maybe even learn…

By effects:
1. free advertising when linked to Google Translate page…
2. some learning on when people want to check the translation which may be an indicator of the quality…

Future of work – Law

Automation is also taking over parts of the legal world – automating previously unstructured tasks into structured tasks, such as ““search-and-find type tasks” in electronic discovery, due diligence and contract review”, leaving the tasks not easily automated to humans:

putting all new legal technology in place immediately would result in an estimated 13 percent decline in lawyers’ hours. (source: CAN ROBOTS BE LAWYERS? COMPUTERS, LAWYERS, AND THE PRACTICE OF LAW)

James Yoon, a lawyer in Palo Alto, Calif., recalls 1999 as the peak of the old way of lawyering. A big patent case then, he said, might have needed the labor of three partners, five associates and four paralegals. Today, a comparable case would take one partner, two associates and one paralegal. (VL: this is a reduction of 75%)

As a lot of the legal work can not yet be easily automated as it is based on complex decision making or just plain “showing up (in court proceedings, talking to clients etc.), the algorithms need to be trained by humans to achieve adequate results. This is a time consuming process, but when it’s done it is quite robust and systematic.

Similar to the master craftsman in the automobile industry, lawyers will have to specialize and let semi-skilled workers and machines do the rest of the work. Routine tasks will be commoditized and cheap, largely done by algorithms and assisted labor:

The data-driven analysis technology is assisting human work rather than replacing it. Indeed, the work that consumes most of Mr. Yoon’s time involves strategy, creativity, judgment and empathy — and those efforts cannot yet be automated. Mr. Yoon, who is 49, stands as proof. In 1999, his billing rate was $400 an hour. Today, he bills at $1,100 an hour. “For the time being, experience like mine is something people are willing to pay for,” Mr. Yoon said. “What clients don’t want to pay for is any routine work.” But, he added, “the trouble is that technology makes more and more work routine.”

A.I. Is Doing Legal Work. But It Won’t Replace Lawyers, Yet.

“Where the technology is going to be in three to five years is the really interesting question,” said Ben Allgrove, a partner at Baker McKenzie, a firm with 4,600 lawyers. “And the honest answer is we don’t know.”

Future of Work – Banking – AI and investing at BlackRock

Apart from the Mergers and Acquisition process and other parts of banking (see previous posts), stock picking is becoming driven by A.I: and human stock pickers are being laid off at Black Rock, one of the largest investment firms globally.

Now, after years of deliberations, Laurence D. Fink, a founder and chief executive of BlackRock, has cast his lot with the machines. On Tuesday, BlackRock laid out an ambitious plan to consolidate a large number of actively managed mutual funds with peers that rely more on algorithms and models to pick stocks.

This has been part of a larger trend for years, showing that most fund managers do not outcompete indices after cost:

Last year, for example, $423 billion left actively managed stock funds and $390 billion poured into index funds, according to Morningstar.

At BlackRock, Machines Are Rising Over Managers to Pick Stocks

The initiative is the most explicit action by a major fund management firm in reaction to the exodus of investors from actively managed stock funds to cheaper funds that track every variety of index and investment theme. Some $30 billion in assets (about 11 percent of active equity funds) will be targeted, with $6 billion rebranded BlackRock Advantage funds.

 

Future of Work – Banking – Automation at JP Morgan

JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours

At JPMorgan Chase & Co., a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours. The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of work each year by lawyers and loan officers.

Management miscellenia – Taiichi Ohno’s favourite word – understanding

Taichi Ohno counselled, never codify method, because it is the thinking that is the key. Ohno’s favorite word was understanding.

Like Socrates, who never wrote anything (Plato did). The moment you think the tool is the way, you may stop to think… Learning people to think, that is the way.

From an article by Jan Höglund. Great blog.

Management miscellania – Common misunderstanding # 1 – good – cheap – fast

Statement: you can’t have good quality, fast and cheap at the same time.

Only:
Good and fast, not cheap.
Good and cheap, not fast.
Cheap and fast, not good.

This is a common misunderstanding: it can be all 3.

Why?

The better your processes, the less waste you have.
The less waste you have, the faster you can produce.
The less waste you have, the higher quality you can produce.
The less waste you have, the less resources you need.

So this then leads to higher value per unit of cost/time.

If you want to improve, focus on value added activities / cost.
If you focus on cost, it invariably leads to worse quality by higher cost.

This is the reason why so many cost cutting operations go wrong…

Future of Work – Banking – Automation at Goldman Sachs

Goldman Sachs is busy automating banking – basically substituting classic banking jobs with engineers. This makes sense as banks mainly process information which often can be done at a lower cost with less mistakes by automated systems.
Original article plus synopsis with examples and questions below:

 

Traders are out, computer engineers are in, as Goldman Sachs goes digital

At its height back in 2000, the U.S. cash equities trading desk at Goldman Sachs’s New York headquarters employed 600 traders, buying and selling stock on the orders of the investment bank’s large clients. Today there are just two equity traders left. Automated trading programs have taken over the rest of the work, supported by 200 computer engineers.

Here the examples for trading:
At its height back in 2000, the U.S. cash equities trading desk at Goldman Sachs’s New York headquarters employed 600 traders, buying and selling stock on the orders of the investment bank’s large clients. Today there are just two equity traders left. (…) Goldman Sachs has already begun to automate currency trading, and has found consistently that four traders can be replaced by one computer engineer, Chavez said at the Harvard conference. Some 9,000 people, about one-third of Goldman’s staff, are computer engineers.
Investment banking:
Next, Chavez said, will be the automation of investment banking tasks, work that traditionally has been focused on human skills like salesmanship and building relationships. Though those “rainmakers” won’t be replaced entirely, Goldman has already mapped 146 distinct steps taken in any initial public offering of stock, and many are “begging to be automated,” he said.
Consumer lending:
Goldman’s new consumer lending platform, Marcus, aimed at consolidation of credit card balances, is entirely run by software, with no human intervention, Chavez said. It was nurtured like a small startup within the firm and launched in just 12 months, he said. It’s a model Goldman is continuing, housing groups in “bubbles,” some on the now-empty trading spaces in Goldman’s New York headquarters: “Those 600 traders, there is a lot of space where they used to sit,” he said.
Revenue per employee has gone up, meaning that Goldman can share the pie with a smaller number of people. One third of them are now engineers (average salary at Goldman is 500.000 USD).
Interesting questions might be here:
What exactly is the value created by these people for their customers and wider stakeholders?
And looking at market rates for employment, what part of the salary might be a proper reflection of the skillset of these people and what part is more related to quasi-oligopolic market power like network effects or regulatory protections?