Artificial Intelligence – Part 1


Three things you need to know

Pay attention. Your life is about to be significantly changed by Artificial Intelligence (AI), whether you want it to be or not.

Every once in while something happens that tosses a huge rock into the pond of human affairs. Such rocks include things like the discovery of fire, the invention of the wheel, written language, movable type, the telegraph, computers, and the Internet. These kinds of massive disturbances produce pronounced, remarkable, unexpected changes, and radically alter human life.

Artificial Intelligence is just such a rock, and will produce exactly those kinds of disturbances. We’re not prepared for the tsunami that AI is going to throw at us. AI has been the technology of the future since the 1960s, but one that always seemed just over the horizon, and never arrived. Certainly, AI was widely discussed when I got my degree in computer science more than 30 years ago.

But now AI is becoming a reality and it is going to hit us far faster than we now expect. This will lead to an avalanche of effects that will reach into all aspects of our lives, society, the economy, business, and the job market. It will lead to perhaps the most dramatic technological revolution we have yet experienced – even greater than the advent of computers, smartphones or the Internet.

There are three keys to AI that will help to understand what’s happening:

  • AI is the Swiss Army knife of technology
  • AI is not a shrink-wrapped product, and
  • Once AI is properly established, the domino effects occur with astonishing speed.

But before I dive into these three keys, let me tackle what AI is because there is no real agreement what the term “artificial intelligence” means. I read one article, for instance, that claimed that there are 33 kinds of AI. And, indeed, the term covers a broad range of techniques and technologies.

But in my view, they all have a central, defining characteristic. I define AI as a computer system that is adaptive and can solve problems it has not encountered before. Some of those problems are ones humans have solved – but increasingly, many of such problems are ones humans haven’t solved and might not be able to solve unassisted.

With that in mind, let’s turn to the three keys to AI.

AI Is the Swiss Army Knife of Technology

AI is not restricted to any narrow range of fields or areas of human endeavor, but will be applied anywhere and everywhere where some smarts would be helpful. The highest profile results will be things like robots and self-driving cars, but there are thousands of other places where AI will be used.

Security systems, whether at airports or a local clothing store, will use AI to identify faces, either those that might commit crimes, or those that are more likely to buy something.

It will be used to assess satellite images to locate submarines, pods of whales, which agricultural areas are producing vibrant crops and which are suffering (and hence what will happen to crop prices), or to judge how well traffic is flowing in a city and how it could be improved.

It will be used in cars and home heating systems to determine – on a second-by-second basis – how to most efficiently use fuel while keeping human users happy.

It will manage investment portfolios better than all but the most gifted humans – and be more consistent in their management results than their human counterparts.

It will work in all aspects of health care, energy management, manufacturing, industrial process control, accounting, law, weather and climate prediction, drug discovery, toy design, and entertainment, ranging from responsive, real-time virtual reality to traditional game playing.

It will help chefs design more interesting, nutritious and economical food and help hotels provide more satisfying, more profitable stays. It will watch over babies and the elderly to make sure they’re safe and potential criminals to make sure everyone else is safe.

It will be used to design a unique, virtual newspaper for each subscriber that appeals to that reader’s particular interests.

It will design bridges and desserts and cars and fashions and farms and teeth, and just about anything else that humans use, build or think about.

Bad guys will use it to identify the highest value targets and design cheap, effective explosive devices. They’ll use it to commit identity theft at a rapidly accelerating pace – even as “white hat” hackers use it to thwart evil AI.

Politicians will use AI to identify silent voters who would be inclined to vote for them if asked – and opponents will use it to develop custom-tailored messages to make sure such voters stay home.

It will be used to identify weaknesses in opponents’ armies, or their economies, or their political appeal.

And just about anything else you can think of.

AI will be used everywhere, all the time, and by everyone – whether they know it or not.

AI Is Not a Shrink-Wrapped Product

Using AI is not easy, simple or straightforward. You can’t just take it out of the box, plug it in and start getting fabulous results. It takes three major, difficult-to-achieve things: good data, smart analytics and clear objectives.

AI’s are fundamentally data driven because they use data to interpret patterns and create patterns for which they can search or use to select behaviors or actions. If the data is dirty, meaning it contains errors or too much irrelevant data points, or isn’t timely, which means it’s not indicating what’s happening now, then the results won’t be very useful. This is the classic computer observation, GIGO: “Garbage In, Garbage Out.”

Hence, if you’re trying to get an Artificial Intelligence to help you trade stocks and using data from three months or even three hours ago, you’re not going to get good results as markets don’t stand still.

Smart analytics means having someone identify what patterns the AI system is looking for. If you can’t show the AI how to use the data you’ve provided to clearly analyze what’s happening, then you’re not going to be able guide it to figure out what it needs to do to produce the results you want.

For instance, you can’t get an AI to assess a satellite photo of a farming region and determine whether the region is suffering from drought unless you can provide the analytical tools to tell it what that looks like.

If you want AI to look at tissue samples to determine whether a particular kind of cancer is present, you need to be provided a means to tell when that cancer is present. Sometimes this can be done in a rough way – by presenting thousands of cases where the outcome is already known, then telling the AI, “These samples have the cancer we’re looking for, but these ones don’t.” But at other times, you need to have very precise parameters to inform the AI what to search for, and how to do it, depending on the application and means of discovery.

And finally, you need to define what you consider a successful result to be. OpenAI researcher Dario Amodei showed off an autonomous system that taught itself to play Coast Runners, an old boat-racing video game. The winner is the boat with the most points that also crosses the finish line.

“The result was surprising: The boat was far too interested in the little green widgets that popped up on the screen. Catching these widgets meant scoring points. Rather than trying to finish the race, the boat went point-crazy. It drove in endless circles, colliding with other vessels, skidding into stone walls and repeatedly catching fire.”

The AI obviously thought its objective was simply to get the highest number of points, rather than the highest number of points while finishing the race, and not crashing and burning.

So, unless you can provide the data necessary for an AI to learn what’s successful and what isn’t, have the means of analyzing that data, and have clearly identified what you want the AI to do, you’re not going to get very far in using AI.

Once AI Is Established, the Domino Effects Occur with Astonishing Speed

AlphaGo is a software AI developed by DeepMind, a machine-learning company owned by Alphabet/Google. AlphaGo was developed to play the traditional Asian game of Go, which is much more difficult to master than chess. Computer scientists speculated that it would take AlphaGo 15-20 years to become competitive with the best human players.

Yet AlphaGo beat Lee Sedol, the world champion, four games out of five in March of 2016 – two years after it was created.

In November of 2015, a company called Kensho unveiled an AI to evaluate and summarize the monthly Bureau of Labor Statistics’ (BLS) monthly employment report. The Kensho AI compared the BLS report with statistics from dozens of other databases, and produced a summary, and 13 key exhibits, along with a forecast of how it would affect dozens of investments, based on how they had responded to earlier reports. It used to take 2-5 days for an experienced and intelligent research analyst, working full-time, to do this. Kensho produced and distributed this report on its own, within minutes of the release of the BLS report – and does the same with many other kinds of economic and financial data.

Even the so-called “Masters of the Universe” ­– the highly paid, high profile institutional stock traders on Wall Street – aren’t immune:

“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.”

Note that these 600 traders probably made $500,000 or more each back in 2000. Now they’ve been put out of work by AI.

The legal profession seems to be particularly susceptible to early occupation by AIs:

“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.”

So, before June of 2017, lawyers and loan officers spent 360,000 hours a year interpreting commercial loan agreements for JPMorgan Chase. Since then, that specific kind of work has vanished.

ROSS is a computer system based on IBM’s Watson AI platform. ROSS performs legal research and prepares legal briefs. In so doing, it has the potential to replace the work done by hundreds or thousands of paralegals and junior lawyers. Is the law profession concerned?

“[A recent] survey of large U.S. law firms… asked whether Watson would replace various timekeepers in these firms in the next five to 10 years. Half the respondents said it would replace paralegals, 35 percent said first-year associates. …the other interesting aspect of that survey was the response to the option ‘Computers will never replace human practitioners.’ That got a 46 percent affirmative response four years ago; this time around, just 20 percent. That’s a huge drop.”

Finally, let me offer a personal anecdote. Not long ago I had a conversation with a computer scientist who has clients in the financial industry. He confided in me that most people didn’t realize how quickly the domino effects cascade once an AI is properly established. “Once a front-line job can be done by AI,” he said, “then usually all of the back-office jobs that support it can also be replaced. Companies have no idea how fast this is happening.”

He didn’t want to go public with his thoughts because he was afraid it would scare his company’s clients.

So, AI is coming, and it’s coming far faster than people realize and the consequences will be far-reaching. The question is what happens next? Read part two of Richard Worzel’s column in the spring 2018 edition of Western Equipment Dealer.

RICHARD WORZEL is a chartered financial analyst (CFA), best-selling author, and one of today’s leading futurists, trend analysts, and innovation specialists. He is also a professional member of the World Future Society. Worzel helps corporations and industry associations plan intelligently for the future. His client list includes Coca-Cola, Ford, IBM, Bell, the U.S. Navy Department of Medicine & Surgery, the National Research Council, the Clerk of the House of Commons of Canada, among many others. To learn more, visit Future Search at


About Author

Leave A Reply