the next wave of innovation
Governments today are teetering on a tightrope — and it's not a comfortable one. On one hand, there is AI innovation, which holds the promise of quicker healthcare diagnoses, more intelligent public services, and even economic expansion through industries powered by technology. On the other hand, thRead more
Governments today are teetering on a tightrope — and it’s not a comfortable one.
On one hand, there is AI innovation, which holds the promise of quicker healthcare diagnoses, more intelligent public services, and even economic expansion through industries powered by technology. On the other hand, there is data privacy, where the stakes are intensely personal: individuals’ medical records, financial information, and private discussions.
The catch? AI loves data — the more, the merrier — but privacy legislation is meant to cap how much of it can be harvested, stored, or transmitted. Governments are thus attempting to find a middle ground by:
Establishing clear limits using regulations such as GDPR in Europe or new AI-specific legislation that prescribes what is open season for data harvesting.
Spurring “privacy-first” AI — algorithms that can be trained on encrypted or anonymized information, so personal information never gets shared.
Experimenting sandbox spaces, where firms can try out AI in controlled, overseen environments before the public eye.
It’s a little like having children play at a pool — the government wants the enjoyment and skill development to occur, but they’re having lifeguards (regulators) on hand at all times.
If they move too far in the direction of innovation, individuals will lose faith and draw back from cooperating and sharing information; if they move too far in the direction of privacy, AI development could grind to a halt. The optimal position is somewhere in between, and each nation is still working on where that is.
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Neurosymbolic AI: Merging Intelligence with Logic Think of neurosymbolic AI as the combination of two types of intelligence. Here you have neural networks. They provide powerful pattern recognition for messy, unstructured data from the real world including image, voice, and sensor data. Here you havRead more
Neurosymbolic AI: Merging Intelligence with Logic
Think of neurosymbolic AI as the combination of two types of intelligence. Here you have neural networks. They provide powerful pattern recognition for messy, unstructured data from the real world including image, voice, and sensor data. Here you have symbolic reasoning, a powerful way to apply rules, logic, and structured knowledge to formal problem solving.
How may we combine both of these approaches? Each approach is great on its own. Today’s AI can very well detect a cat in an image and very well solve a logic puzzle, but it cannot do both together. Neurosymbolic AI makes this possible. It can:
1. Reason and explain its decisions—not just give answers but explain why those answers are valid
2. Learn quickly—as it encounters new patterns, it can not only rely on the new knowledge but also relate what it has already learned, instead of having to start with zero application and comprehension.
3. Recognize and account for uncertainty better. Neurosymbolic AI can apply logic when data is articulated clearly, and learn when it is messy.
In the next technological wave, we may see AI reading complex legal contracts, teasing out the author’s intent, and reasoning toward implications. Or we may see medical AI that integrates lab tests and established care guidelines toward timely and safe diagnoses.
Neurosymbolic AI provides an AI with something resembling an “intuition”
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