Why We Should Fear and Hail A.I.
I recently had the pleasure of speaking with Neil Sahota for an episode of the Future Squared podcast.
Sahota is an IBM Master Inventor, United Nations A.I. subject matter expert, professor at UC Irvine.
He’s a founding member of the UN’s Artificial Intelligence for Social Good Committee, and he co-wrote Own the A.I. Revolution, which provides a future-forward look at A.I., focusing on how businesses can use it to commercialize while doing good in the world.
We explored the depths of the esoteric, covering questions like:
…and so much more.
3. Bursting the Jargon bubbles — Deep Learning
4. How Can We Improve the Quality of Our Data?
You can listen to the entire conversation below.
Episode #362: Own the AI Revolution with Neil Sahota
Neil Sahota is an IBM Master Inventor, United Nations A.I. subject matter expert, professor at UC Irvine, and…
www.futuresquared.xyz
As I like to do, to both reiterate my own learning, and share lessons learned with the world, find below key take-aways from my conversation with Neil Sahota.
So many companies peddle what they purport to be A.I., including IBM, but for a product to truly earn this definition, it needs to meet the following three criteria.
1 — it learns from experience and consumptions
2 — understands natural language
3 — interacts like a human being
Based on these criteria, IBM’s Watson is not actually A.I., despite its success at Jeopardy all of those years ago!
As Berkeley’s John Searle noted, Watson manipulates symbols but doesn’t understand the meaning behind the symbols as a human would.
AGI: artificial general intelligence (think human cognition — we are not there yet)
ANI: artificial narrow intelligence (think A.I. that does just one thing really well — this is where we are at today. eg. Google Translate)
It’s difficult to determine how far away AGI. It could be next month or could be 50 years away.
Commercial incentives don’t support development of AGI.
There are much better rewards to be reaped in the short-term based on investing in narrow A.I., for which there are numerous use cases, and for which the cost of investment and unknowns are much lower. This makes the ROI higher on narrow A.I., and delays the development of AGI.
AI helps us make better decisions, but it has three key challenges:
People might have different experiences of A.I., as we do with Google search.
This means we might develop different world views as a consequence.
As Sahota put it, “the truth may change but the facts remain the same”.
The paperclip maximizer is a thought experiment showing how AGI, even one designed competently and without malice, could ultimately destroy humanity. An extremely powerful A.I. could seek goals that are completely alien to ours, and as a side-effect destroy us by consuming resources essential to our survival.
This tendency to optimize for a particular outcome, at the expense of ethics, morality or reason, is known as ‘perverse instantiation’.
Sahota says that the paperclip maximizer is real, insofar as it is a possibility. “It could actually happen”. In order to counter this, we need to set constraints to avoid adverse outcomes. With so many potential constraints to account for, this represents challenges.
A.I. may, in a single generation, produce more technological breakthroughs than humankind has managed during the first 20,000 years of its existence.
47% of US jobs are likely to be automated by 2050.
The goal of A.I. is to free people up for higher-value tasks.
“Jobs will go away, but new jobs will be created”, says Sahota.
On the other side of the spectrum, many fear that there won’t be enough work to go around, which is why universal basic income (UBI) has become such a big talking point as of late.
Since 1980, the gap between US productivity and labor compensation has gotten larger, thanks to technology doing more of what humans once did.
This gap is set to get larger and with each disruptive innovation, it can take decades for organizations and societies to reorganize around it, during which time we might experience a downturn in both productivity and potentially, average living standards. This was true of the transition from steam to electricity, and it is known in economic circles as a ‘productivity paradox’.
Given the pushback against big-tech that we’re seeing, thanks partially to the attention merchant economy and the Big Brother nature of companies like Facebook, it’s critical that we embed philosophy and the arts into the design of technology.
The challenge then becomes, which philosophy? Since Socrates, there have been debates between rival schools of philosophy, and ideas in general. But the need to reconcile the gaps between being able to commercialize technology, and being able to commercialize technology that does good for humanity, is painfully evident each time you walk onto a Subway train. I’m talking about the army of people mindlessly staring at their screens.
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To help you avoid stepping into these all too common pitfalls, we’ve reflected on our five years as an organization working on corporate innovation programs across the globe, and have prepared 100 DOs and DON’Ts.
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