Artificial intelligence is seeping its way into every industry. Before long, your business will take advantage of this growing technology, and if not, your competitors certainly will. But keeping up with every advance in AI is overwhelming. The rapid growth of this technology and consistent research breakthroughs render it difficult for any layman to keep abreast of it all.
Rather than write an AI algorithm to track the topic, I’ve taken it upon myself to seek out a collection of articles that will introduce AI beginners to the marvel of machine learning and its impact on business and society at large. Let’s begin with a quick history lesson.
With links to critical research papers and citations, Dormehl summarizes the milestones in the history of artificial intelligence. If this is your first foray into AI, Dormehl provides a light and fun introduction to the math and technology behind AI.
Jason Furman, John P. Holdren, Cecilia Munoz, Megan Smith, and Jeffrey Zients
Released in December 2016 by the office of the president of the United States, this report summarizes the potential economic impact of artificial intelligence on the country. The report suggests strategies that the United States may use to capitalize on the burgeoning technology without cannibalizing its labor force:
AI-driven automation can act—and in some cases has already acted—as a shock to local labor markets that can initiate long-standing disruptions. Without some form of transfers and safety net, the economic benefits of AI-driven automation may not be shared by all, and some workers, families, and communities may be persistently negatively affected.
While neither current nor future administrations are beholden to follow the recommendations made by its authors, this document remains a well-researched and articulated perspective on how artificial intelligence may impact the United States and its citizenry.
Now that we’ve gotten the US point of view, time to let China weigh in. It’s no secret that China is investing heavily in AI and would like to be the world’s number 1 AI technology provider. Currently, as far as funding goes, they still hold the number 2 spot, but the gap is closing. In this article filled with statistics and movie references, written by technology reporter and Beijing resident Masha Borak, the message is clear: China’s tech industry is ready to go head-to-head with IBM, Google, and the like. With indications that the current US administration is not planning to open its coffers very wide for AI, China’s attempt to grab the lion’s share of the market might come to fruition sooner than we think.
Let’s turn away from politics and look to business. While investment in artificial intelligence in the nation of Bill Gates and Mark Zuckerberg is being challenged, there is no doubt in Jim Breyer’s mind that AI will produce another one of them—somewhere. According to Breyer, someone or some group, potentially you and your company, will take advantage of AI in such a way as to generate a yet-unseen level of wealth:
"[...] there is room for a new generation of company that is applying AI and self-learning algorithms in healthcare and finance and energy that will generate billions of market cap."
My critical take away from this article is that while most tech firms look to improve their offerings and processes through artificial intelligence, none has built an AI product from the ground up that brings a dramatic, life-changing value to the world.
No one has broken the barrier of mega-wealth with artificial intelligence, but Andrew Ng wants to change that. Ng’s company, Landing, aims to automate dozens if not hundreds of manual assembly line tasks that presently require human intervention. These tasks include spotting deficiencies in products, adjusting equipment configuration, and tweaking processes to optimize resource use:
[…] Ng claims that his startup won’t leave factory workers worse off. “AI can make a society where everything is much better and humans are freed from mental drudgery,”
Erik Brynjolfsson, Andrew McAfee
Brynjolfsson and McAfee do a wonderful job of defining precisely how business uses artificial intelligence and machine learning to automate tasks, improve workflow, and generate value for customers. This article also provides an introduction to the machinations of these technologies: how exactly does a computer spot defects in a microchip? If you’re inflicted by that conundrum, this article has the cure. Beyond definitions, this piece will also inspire you to look for opportunities to employ AI in your own organization:
The final piece of good news, and probably the most underappreciated, is that you may not need all that much data to start making productive use of [machine learning]. [… If] success is defined instead as significantly improving performance, then sufficient data is often surprisingly easy to obtain.
With a firm understanding of how business can take advantage of AI, you’re ready to look at raw data and projections surrounding that very thing. Columbus has collected analysis from over two dozen sources to provide trends in patent awards, external investments, pilot projects, internal company initiatives, industry leaders, and more. For a quick (or in-depth) report on the trajectory of AI research, investment, and adoption, look no further.
One of the fastest growing areas of machine learning IP is the development of custom chipsets. Deloitte Global is predicting up to 800K machine learning chips will be in use across global data centers this year. Enterprises are increasing their research, investment, and piloting of machine learning programs in 2018.
In the first article on this list, Luke Dormehl noted that among the landmark innovations that brought us today’s artificial intelligence, an algorithm from the 60s was of particular importance. Illustrated in an earlier graphic, backpropagation is a process whereby an artificial intelligence algorithm may edit itself to achieve superior results. When machine learning engineers combine backpropagation with supervised learning techniques, they create the familiar artificial intelligence agents we know today: object recognition services, speech transcribers, and others. Beneath each one resides a neural network instantiated by a human, revised by a machine.
The millions of algorithmic revisions made by backpropagation are inscrutable to the human mind—no human knows precisely how Google’s neural network detects a face in an image; it just does. Will Knight reminds us that as we supplant humans with neural network agents in life-critical tasks such as driving, flying, and warfare, we place our lives at the mercy of capricious actors:
[… At] some stage we may have to simply trust AI’s judgment or do without using it. Likewise, that judgment will have to incorporate social intelligence. Just as society is built upon a contract of expected behavior, we will need to design AI systems to respect and fit with our social norms. If we are to create robot tanks and other killing machines, it is important that their decision-making be consistent with our ethical judgments.
This list has articles beaming with optimism and articles radiating trepidation; beginning with Tim Urban, you will read a blend at both extremes. Tim is a renowned blogger known for thorough investigations into complex and varied topics. In 2015 he published two posts that when combined, elucidated the mysteries behind artificial intelligence technologies, introduced readers to the threat of an artificial superintelligence (ASI), and contrasted potential consequences immediately following the creation of a superintelligence. This first post recaps the history of artificial intelligence and ends with this worrying tidbit:
If our meager brains were able to invent wifi, then something 100 or 1,000 or 1 billion times smarter than we are should have no problem controlling the positioning of each and every atom in the world in any way it likes, at any time—everything we consider magic, every power we imagine a supreme God to have will be as mundane an activity for the ASI as flipping on a light switch is for us.
In Urban’s second post, Tim consolidates opinions from today’s leading philosophers. He divides these perspectives into a four-quadrant grid whose axes represent pessimism on the horizontal, and timeliness of ASI’s arrival on the vertical. He goes on to explain what both optimistic and pessimistic experts believe will occur the moment at which humans produce the first artificial superintelligence. On the optimistic side, people believe the ASI will act benevolently and help humanity become the first immortal species by discovering scientific breakthroughs previously insurmountable: cures for cancer, anti-aging, endlessly regenerative cells, and more.
The pessimistic camp, however:
It seems weird that a story about a handwriting machine turning on humans, somehow killing everyone, and then for some reason filling the galaxy with friendly notes is the exact kind of scenario Hawking, Musk, Gates, and Bostrom are terrified of. But it’s true. And the only thing that scares everyone on Anxious Avenue more than ASI is the fact that you’re not scared of ASI.
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Having read these articles, you’re more aware than most about artificial intelligence, its origins, its prospects, and its potential existential impact on the human race. If I missed any important pieces, please leave us a note on Twitter.
This post was written and generated by Stanley Idesis: human flesh-bag No. 199328842.