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Outsmarting AI. Brennan Pursell
Читать онлайн.Название Outsmarting AI
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isbn 9781538136256
Автор произведения Brennan Pursell
Жанр Банковское дело
Издательство Ingram
Singularity technophiles are like religious sects, who, over the centuries, based on their reading of the Bible, pinpointed the time and place of Jesus’ return to earth. Similar gatherings go on in the United States today. The result is always the same. The heavens are not torn asunder, and the Messiah does not descend in Glory. The group recalculates, pushing off the date by a few decades, or centuries, in an attempt to save face.
Bad or unrealistic AI deployments can cost you big-time. MD Anderson, the University of Texas’s cancer center, burned through $62 million trying to get IBM’s Watson (AI services and toolkit) to automate their cancer diagnoses and treatments. IBM pocketed at least $39 million for Watson’s data processing, and PricewaterhouseCoopers another $21 million to develop and manage the business plan. Believers at MD Anderson claimed that leukemia was all but cured, but after $62 million, not one patient had been treated. The project was canned as quietly as possible. The whole venture was shady to begin with: A multimillionaire from Malaysia, Low Taek Jho (often called Jho Low), supposedly put up $50 million.[6] This is the same Low Taek Jho who then allegedly helped to defraud the Malaysian government of a few billion US dollars from the 1MDB fund, managed by Goldman Sachs. Ugh.
If there is such a thing as easy money, all too often it comes with high costs in other ways, no?
The last two myths we can dispense with in short order.
Myth 6: AI Drives Businesses
AI does not drive your organization; you and your coworkers do. AI will not take over your business or your life, but you would be wise to make use of it, as it best fits your needs. It can enhance your knowledge about your customers and your own coworkers. It can streamline some operations, help automate mind-numbing work, and lighten the load in some tasks.
AI processes data, and data on their own have no decision-making power. Analyzing data can tell you things about your customers, your suppliers, your partners, and your coworkers, but only in part. I think all would agree that working with people is the most complicated part of any business.
Never believe that by getting this or that AI system, you will be able to put this or that business function on autopilot and tune out. That rarely if ever ends well.
Myth 7: AI Will Control Your Mind
The opposite of this myth is true. AI systems at Google and Facebook process oceans of data and classify you as this or that for advertisers willing to pay them for the results, but it’s up to you to buy their goods and services.
To integrate AI successfully into your business, you will have to work with AI vendors or your own team of coders, data scientists, and project managers, but you should never defer to AI outputs—at least, not totally or unconditionally. By all means, take them into account, but remember that people make the decisions and bear the responsibility, not machines.
Many people may have a deep-seated longing to have the perfect servant—one that anticipates your every need and whim, one that never complains, is always quick to respond, and provides pure convenience without a hint of trouble or nuisance. Iron Man has his Jarvis and Friday. The commercial success of Amazon’s Alexa, Google’s Home and Assistant, Apple’s Siri, and Microsoft’s Cortana, despite their severe limitations, indicates just how many people share that desire.
But AI will never tell you what it is that you really want. No AI system will know you better than you do yourself. Unless you lie to yourself. And some people, unfortunately, do that very well.
Having done away with these myths, let’s agree on AI common sense. Working with AI requires all your intelligence and diligence. Below are seven important rules for getting it right.
Rule 1: Data Is the Mother of AI
We don’t want to take this metaphor too far, but think of an AI system as a family. Data is the mother, and if Mom isn’t happy, no one is happy.
Data is where every AI system begins. AI depends on data quality and quantity. “Garbage in, garbage out,” is still the rule. From biased data come biased results, bad business decisions, and big potential legal problems. You will need to bestow a lot of love on your data. You need to compile it, integrate it, and shatter the silos that prevent you from bringing it together. You will need to prepare it, repair it in places, and maintain it.
You will have to work with both “structured” data—the type your algorithms can search and query easily—and “unstructured” data—pretty much everything else—the kinds of things you can readily understand but a computer really can’t. Entries in tables for names, addresses, purchases, etc., are typical of structured data. Unstructured data include texts, posts, images, sound clips, and videos. These files, while digitized like everything else on a computer, are not neatly arranged into rows and columns. An AI system will have to do a lot of calculations to classify what’s in them, but you can train it, and dumb as it is, it works immeasurably faster than you do.
Without data, AI can do nothing. AI can process structured and unstructured data and present information about it in a manageable way. (You will come back to data in chapter 5.)
Rule 2: Math Is the Father of AI
AI is just math! Math and its companion, statistics.
Coded AI systems are expressions of mathematics and logic. Statistics rely on the same. AI algorithms use calculus and linear algebra to work over data in numeric form to get results. The math can get very complicated and sophisticated, but for all that, it’s still math.
An honest AI pro tweeted: “It’s AI when you’re trying to raise money, machine learning when you’re trying to hire developers, and statistics when you’re actually doing it.” This says it all. Statistics is just applied mathematics, in AI, for data analysis.
You may well be wondering, “So if AI is just math, mathematical procedures done on numeric data, then how is AI different from data analytics, predictive analytics, data science, and big data?” Well, they are all part of the same family of algorithms performed by software. A key difference among AI algorithms is their ability to self-optimize—some would say, “to learn.” (We will revisit this important matter in chapter 2.)
The beauty of recent AI software advances is that you do not need to learn, memorize, and key in the algorithms in order to get the outputs you need from your data. Nor do you need specialized hardware. You can even build your own AI data analytics system online in the cloud by dragging and dropping elements into place.
Rule 3: AI Systems Are like Kids—They’re All Unique
For those of you who were offended by the gendered parenting roles in the preceding two sections, please forgive and get over it. We abandon all gender references when it comes to the kids.
AI systems are like children: Each is unique. You could compare them to fingerprints or snowflakes for the same reason: No two copy each other exactly as they do their work, even if they do the same job and process similar data. And in many cases, we are not quite sure how they actually come up with the results that they do, even if we know the data they come from.
AI algorithms adjust their inner workings according to the results they are trained to output, given the inputs. In that sense, you could almost call them “organic,” if not really “alive.”
If you adopt an AI system into your organization to improve one of your business processes, then it will rapidly become your own, unique tool. AI software does not come “out of the box,” and even if you do use a vendor’s software-as-a-service (SaaS), trained on your data, the AI system will really be all your own.