Discussion of concrete near-to-middle term trends in AI

Punoxysm

Instead of prognosticating on AGI/Strong AI/Singularities, I'd like to discuss more concrete advancements to expect in the near-term in AI. I invite those who have an interest in AI to discuss predictions or interesting trends they've observed.

This discussion should be useful for anyone looking to research or work in companies involved in AI, and might guide longer-term predictions.

With that, here are my predictions for the next 5-10 years in AI. This is mostly straightforward extrapolation, so it won't excite those who know about these areas but may interest those who don't:

Speech Processing, the task of turning the spoken words into text, will continue to improve until it is essentially a solved problem. Smartphones and even weaker devices will be capable of quite accurately transcribing heavily-accented speech in many languages and noisy environments. This is the simple continuation of the rapid improvements in speech processing that have allowed brought us from Dragon Naturally-Speaking to Google Now and Siri.
Assistant and intent-based (they try to figure out the "intent" of your input) systems, like Siri, that need to interpret a sentence as a particular command they are capable of, will become substantially more accurate and varied and take cues like tone and emphasis into account. So for example, if you're looking for directions you won't have to repeat yourself in an increasingly loud, slowed and annoyed voice. You'll be able to phrase your requests naturally and conversationally. New tasks like "Should I get this rash checked out" will be available. A substantial degree of personalization and use of your personal history might also allow "show me something funny/sad/stimulating [from the internet]".
Natural language processing, the task of parsing the syntax and semantics of language, will improve substantially. Look at this list of traditional tasks with standard benchmarks: on Wikipedia. Every one of these tasks will have a several percentage point improvement, particularly in the understudied areas of informal text (Chat logs, tweet, anywhere where grammar and vocabulary are less rigorous). It won't get so good that it can be confused with solving AI-complete aspects of NLP, but it will allow vast improvements in text mining and information extraction. For instance, search queries like "What papers are critical of VerHoeven and Michaels '08" or "Summarize what twitter thinks of the 2018 superbowl" will be answerable. Open source libraries will continue to improve from their current just-above-boutique state (NLTK, CoreNLP). Medical diagnosis based on analysis of medical texts will be a major area of research. Large-scale analysis of scientific literature in areas where it is difficult for researchers to read all relevant texts will be another. Machine translation will not be ready for most diplomatic business, but it will be very very good across a wide variety of languages.
Computer Vision, interpreting the geometry and contents of images an video, will undergo tremendous advances. In act, it already has in the past 5 years, but now it makes sense for major efforts, academic, military and industrial, to try to integrate different modules that have been developed for subtasks like object recognition, motion/gesture recognition, segmentation, etc. I think the single biggest impact this will have will be the foundation for robotics development, since a lot of the arduous work of interpreting sensor input will be partly taken care of by excellent vision libraries. Those general foundations will make it easy to program specialist tasks (like differentiating weeds from crops in an image, or identifying activity associated with crime in a video). This will be complemented by a general proliferation of cheap high-quality cameras and other sensors. Augmented reality also rests on computer vision, and the promise of the most fanciful tech demo videos will be realized in practice.
Robotics will advance rapidly. The foundational factors of computer vision, growing availability of cheap platforms, and fast progress on tasks like motion planning and grasping has the potential to fuel an explosion of smarter industrial and consumer robotics that can perform more complex and unpredictable tasks than most current robots. Prototype ideas like search-and-rescue robots, more complex drones, and autonomous vehicles will come to fruition (though 10 years may be too short a time frame for ubiquity). Simpler robots with exotic chemical sensors will have important applications in medical and environmental research.

This discussion should be useful for anyone looking to research or work in companies involved in AI, and might guide longer-term predictions.

With that, here are my predictions for the next 5-10 years in AI. This is mostly straightforward extrapolation, so it won't excite those who know about these areas but may interest those who don't:

Speech Processing, the task of turning the spoken words into text, will continue to improve until it is essentially a solved problem. Smartphones and even weaker devices will be capable of quite accurately transcribing heavily-accented speech in many languages and noisy environments. This is the simple continuation of the rapid improvements in speech processing that have allowed brought us from Dragon Naturally-Speaking to Google Now and Siri.
Assistant and intent-based (they try to figure out the "intent" of your input) systems, like Siri, that need to interpret a sentence as a particular command they are capable of, will become substantially more accurate and varied and take cues like tone and emphasis into account. So for example, if you're looking for directions you won't have to repeat yourself in an increasingly loud, slowed and annoyed voice. You'll be able to phrase your requests naturally and conversationally. New tasks like "Should I get this rash checked out" will be available. A substantial degree of personalization and use of your personal history might also allow "show me something funny/sad/stimulating [from the internet]".
Natural language processing, the task of parsing the syntax and semantics of language, will improve substantially. Look at this list of traditional tasks with standard benchmarks: on Wikipedia. Every one of these tasks will have a several percentage point improvement, particularly in the understudied areas of informal text (Chat logs, tweet, anywhere where grammar and vocabulary are less rigorous). It won't get so good that it can be confused with solving AI-complete aspects of NLP, but it will allow vast improvements in text mining and information extraction. For instance, search queries like "What papers are critical of VerHoeven and Michaels '08" or "Summarize what twitter thinks of the 2018 superbowl" will be answerable. Open source libraries will continue to improve from their current just-above-boutique state (NLTK, CoreNLP). Medical diagnosis based on analysis of medical texts will be a major area of research. Large-scale analysis of scientific literature in areas where it is difficult for researchers to read all relevant texts will be another. Machine translation will not be ready for most diplomatic business, but it will be very very good across a wide variety of languages.
Computer Vision, interpreting the geometry and contents of images an video, will undergo tremendous advances. In act, it already has in the past 5 years, but now it makes sense for major efforts, academic, military and industrial, to try to integrate different modules that have been developed for subtasks like object recognition, motion/gesture recognition, segmentation, etc. I think the single biggest impact this will have will be the foundation for robotics development, since a lot of the arduous work of interpreting sensor input will be partly taken care of by excellent vision libraries. Those general foundations will make it easy to program specialist tasks (like differentiating weeds from crops in an image, or identifying activity associated with crime in a video). This will be complemented by a general proliferation of cheap high-quality cameras and other sensors. Augmented reality also rests on computer vision, and the promise of the most fanciful tech demo videos will be realized in practice.
Robotics will advance rapidly. The foundational factors of computer vision, growing availability of cheap platforms, and fast progress on tasks like motion planning and grasping has the potential to fuel an explosion of smarter industrial and consumer robotics that can perform more complex and unpredictable tasks than most current robots. Prototype ideas like search-and-rescue robots, more complex drones, and autonomous vehicles will come to fruition (though 10 years may be too short a time frame for ubiquity). Simpler robots with exotic chemical sensors will have important applications in medical and environmental research.

Academic CV researchers will write a lot of papers, but there won't be any big commercial successes that are based on dramatic improvements in CV science. This is a subtle point: there may be big CV successes, but they will be based on figuring out ways to use CV-like technology that avoids grappling with the real hardness of the problem

Do driverless cars that drive on normal streets count?

Driverless cars are actually a good illustration of my point. These cars use CV at some level, but they depend fundamentally on laser range finders, GPS, and precompiled road maps. There's no way modern CV alone could work reliably enough in such a potentially dangerous and legally fraught situation.

23

Discussion of concrete near-to-middle term trends in AI

23

23

23

Discussion of concrete near-to-middle term trends in AI

23

23