Issue 90: When Machine Learning Goes Wrong

Issue 90: When Machine Learning Goes Wrong

The People of Ukraine are not forgotten.
The Tufts University newspaper published an article this week about
a multinational effort
to preserve the digital and digitized cultural heritage of the country.
On the other side of the war,
Russian citizens are downloading Wikipedia
out of fear of more drastic network filtering or collapse of Russia’s connections to the global internet.
Eleven years ago this week, the judge overseeing the Google Book Search case (
Authors Guild v. Google
) ruled that the proposed settlement was not «not fair, adequate, and reasonable.»
As you might recall, the proposal was for a grand vision of a book author rights clearinghouse—not unlike what is in place for the music industry.
I had a
Thursday Threads
entry that
covered the initial reactions from the litigants, legal observers, and the library community
.
In writing this week’s article, I learned that machine learning is a subset of the artificial intelligence field.
While the terms are often used interchangeably, machine learning is one part of artificial intelligence.
As the
Columbia University Engineering Department describes it
, «put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data.»
With that definition in mind, the thread this week is on challenges with machine learning:
Flip the Switch on Your Drug Synthesizing Tool and Chemical Weapons Come Out
With Machine Learning, Garbage In/Garbage Out
Five Realities Why Applying Machine Learning to Medical Records is Hard
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Flip the Switch on Your Drug Synthesizing Tool and Chemical Weapons Come Out
This generative model normally penalizes predicted toxicity and rewards predicted target activity. We simply proposed to invert this logic by using the same approach to design molecules de novo, but now guiding the model to reward both toxicity and bioactivity instead.
In less than 6 hours after starting on our in-house server, our model generated 40,000 molecules that scored within our desired threshold. In the process, the AI designed not only VX, but also many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible.
—Urbina, F., Lentzos, F., Invernizzi, C.
et al.
Dual use of artificial-intelligence-powere…