Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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What Are The Biggest Unresolved Problems In Artificial Intelligence?

By Nand Kishor |Email | Oct 31, 2017 | 11967 Views

What is the biggest unresolved problem for AI? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by Yohan John, Neuroscience PhD, on Quora:

I think the best way to understand all the things that AI is missing is to describe a single example situation that folds together a variety of cognitive abilities that humans typically take for granted. Contemporary AI and machine learning (ML) methods can address each ability in isolation (to varying degrees of quality), but integrating these abilities is still an elusive goal.

Imagine that you and your friends have just purchased a new board game - one of those complicated ones with an elaborate board, all sorts of pieces, decks of cards, and complicated rules. No one yet knows how to play the game, so you whip out the instruction booklet. Eventually you start playing. Some of you may make some mistakes, but after a few rounds, everyone is on the same page, and is able to at least attempt to win the game.

What goes into the process of learning how to play this game?

Language parsing: The player reading from the rule book has to turn symbols into spoken language. The players listening to the rules being read aloud have to parse the spoken language.

Pattern recognition: The players have to connect the words being read aloud with the objects in the game. "Twelve sided die" and "red soldier" have to be identified based on linguistic cues. If the instruction booklet has illustrations, these have to be matched with the real-world objects. During the game, the players have to recognize juxtapositions of pieces and cards, and key sequences of events. Good players also learn to recognize patterns in each others' play, effectively creating models of other people's mental states.

Motor control: The players have to be able to move pieces and cards to their correct locations on the board.

Rule-following and rule inference: The players have to understand the rules and check if they have been applied correctly. After the basic rules have been learned, good players should also be able to discover higher-level rules or tendencies that help them win. Such inferences strongly related to the ability to model other peoples' minds. (This is known in psychology as theory of mind.)
Social etiquette: The players, being friends, have to be kind to each other even if some players make mistakes or disrupt the proceedings. (Of course we know this doesn't always happen.)

Dealing with interruptions: If the doorbell rings and the pizza arrives, the players must be able to disengage from the game, deal with the delivery person, and then get back to the game, remembering things like whose turn it is.

There has been at least some progress in all of these sub-problems, but the current explosion of AI/ML is primarily a result of advances in pattern recognition. In some specific domains, artificial pattern recognition now outperforms humans. But there are all kinds of situations in which even pattern recognition fails. The ability of AI methods to recognize objects and sequences is not yet as robust as human pattern recognition.

Humans have the ability to create a variety of invariant representations. For example, visual patterns can be recognized from a variety of view angles, in the presence of occlusions, and in highly variable lighting situations. Our auditory pattern recognition skills may be even more impressive. Musical phrases can be recognized in the presence of noise as well as large shifts in tempo, pitch, timbre and rhythm*.

No doubt AI will steadily improve in this domain, but we don't know if this improvement will be accompanied by an ability to generalize previously-learned representations in novel contexts.

No currently-existing AI game-player can parse a sentence like "This game is like Settlers of Catan, but in Space". Language-parsing may be the most difficult aspect of AI. Humans can use language to acquire new information and new skills partly because we have a vast store of background knowledge about the world. Moreover, we can apply this background knowledge in exceptionally flexible and context-dependent ways, so we have a good sense of what is relevant and what is irrelevant.

Generalization and re-use of old knowledge are aspects of a wider ability: integration of multiple skills. It may be that our current approaches do not resemble biological intelligence sufficiently for large-scale integration to happen easily.

A well-known type of integration challenge goes by the name of the symbol grounding problem. This is the problem of how symbols (such as mathematical symbols or words in a language) relate to perceptual phenomena â?? sights, sounds, textures and so on**.

Roughly speaking, artificial methods are of two types: symbolic, and sub-symbolic. Symbolic methods are used in "classic" or "good old fashioned" AI. They can be very useful for deterministic rule-based situations like chess-playing (but we typically have to code up the rules in advance). Symbolic processing works well when humans do the symbol-grounding in advance. It is not so great at dealing directly with 'raw' inputs in the form of light, sound, texture and pressure.

At the other extreme we have sub-symbolic methods such as neural networks (of which deep learning networks are a type). These methods work with digitized versions of raw inputs â?? pixels, sound files and so on. Sub-symbolic methods are great for many forms of pattern recognition and classification, but we don't have reliable methods of going from category labels to symbols that are manipulated in a rule-based fashion.

So in summary, the key to understanding the sheer scale of the artificial intelligence problem requires appreciating that intelligence consists of much more than pattern recognition. What is needed is the ability to link patterns in a bidirectional way with symbolic representations, so that linguistic and rule-based thinking can occur in embodied agents that interact with the real world in real time.

Source: Forbes
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