AI and the Singularity: A Fallacy or a Great Opportunity?
- Machine learning means learning from data; AI is a buzzword. Machine learning lives up to the hype, and there is an incredible number of problems that you can solve by providing the right training data to the right learning algorithms. Call it AI if that helps you sell it, but know that AI, at least as it is used outside of academia, is often a buzzword that can mean whatever people want it to mean.
- Machine learning is about data and algorithms, but mostly data. There is a lot of excitement about advances in machine learning algorithms, and particularly about deep learning. However, data is the key ingredient that makes machine learning possible. You can have machine learning without sophisticated algorithms, but not without good data.
- Unless you have a lot of data, you should stick to simple models. Machine learning trains a model from patterns in your data, exploring a space of possible models defined by parameters. If your parameter space is too big, you will overfit to your training data and train a model that does not generalize beyond it. A detailed explanation requires more math, but as a rule, you should keep your models as simple as possible.
- Machine learning can only be as good as the data you use to train it. The phrase “garbage in, garbage out” predates machine learning, but it aptly characterizes a key limitation of machine learning. Machine learning can only discover patterns that are present in your training data. For supervised machine learning tasks like classification, you will need a robust collection of correctly labeled, richly featured training data.
- Machine learning only works if your training data is representative. Just as a fund prospectus warns that “past performance is no guarantee of future results”, machine learning should warn that it is only guaranteed to work for data generated by the same distribution that generated its training data. Be vigilant of skews between training data and production data, and retrain your models frequently, so they do not become stale.
- Most of the hard work for machine learning is data transformation. From reading the hype about new machine learning techniques, you might think that machine learning is mostly about selecting and tuning algorithms. The reality is more prosaic: most of your time and effort goes into data cleansing and feature engineering—that is, transforming raw features into features that better represent the signal in your data.
- Deep learning is a revolutionary advance, but it is not a magic bullet. Deep learning has earned its hype by delivering advances across a broad range of machine learning application areas. Moreover, deep learning automates some of the work traditionally performed through feature engineering, especially for image and video data. But deep learning is not a silver bullet. You cannot just use it out of the box, and you will still need to invest significant effort in data cleansing and transformation.
- Machine learning systems are highly vulnerable to operator error. With apologies to the NRA, “Machine learning algorithms don’t kill people; people kill people.” When machine learning systems fail, it is rarely because of problems with the machine learning algorithm. More likely, you have introduced human error into the training data, creating bias or some other systematic error. Always be skeptical, and approach machine learning with the discipline you apply to software engineering.
- Machine learning can inadvertently create a self-fulfilling prophecy. In many applications of machine learning, the decisions you make today affect the training data you collect tomorrow. Once your machine learning system embeds biases into its model, it can continue generating new training data that reinforce those biases. And some biases can ruin people’s lives. Be responsible: do not create self-fulfilling prophecies.
- AI is not going to become self-aware, rise up, and destroy humanity. A surprising number of people seem to be getting their ideas about artificial intelligence from science fiction movies. We should be inspired by science fiction, but not so credulous that we mistake it for reality. There are enough real and present dangers to worry about, from consciously evil human beings to unconsciously biased machine learning models. So you can stop worrying about SkyNet and “superintelligence”.
2. Materials and Methods
Consequently, the conclusion that we reached as a result of Siegel’s story and his conclusion is as follows:Here’s how scientists didn’t let it slip away… If all we had done was look at the automated signals, we would have gotten just one “single-detector alert,” in the Hanford detector, while the other two detectors would have registered no event. We would have thrown it away, all because the orientation was such that there was no significant signal in Virgo, and a glitch caused the Livingston signal to be vetoed. If we left the signal-finding solely to algorithms and theoretical decisions, a 1-in-10,000 coincidence would have stopped us from finding this first-of-its-kind event. But we had scientists on the job: real, live, human scientists, and now we’ve confidently seen a multimessenger signal, in gravitational waves and electromagnetic light, for the very first time.
- AI combined with human intervention produces the most desirable results.
- AI by itself will never entirely replace human intelligence.
- One cannot rely on AI, no matter how sophisticated, to always get the right answer or reach the correct conclusion.
Conflicts of Interest
- Tukelang, D. 10 Things Every Small Business Should Know About Machine Learning. Available online: https://www.inc.com/quora/10-things-every-small-business-should-know-about-machine-learning.html (accessed on 29 November 2017).
- Siegel, E. LIGO’s Greatest Discovery Almost Didn’t Happen. Available online: https://medium.com/starts-with-a-bang/ligos-greatest-discovery-almost-didn-t-happen-a315e328ca8 (accessed on 24 April 2018).
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Braga, A.; Logan, R.K. AI and the Singularity: A Fallacy or a Great Opportunity? Information 2019, 10, 73. https://doi.org/10.3390/info10020073
Braga A, Logan RK. AI and the Singularity: A Fallacy or a Great Opportunity? Information. 2019; 10(2):73. https://doi.org/10.3390/info10020073Chicago/Turabian Style
Braga, Adriana, and Robert K. Logan. 2019. "AI and the Singularity: A Fallacy or a Great Opportunity?" Information 10, no. 2: 73. https://doi.org/10.3390/info10020073