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AI: Paradigm Shifts; author explores how AI will change with research
- 1. AI: Paradigm Shifts •AI research trends continue to shift – Moving AI from a stand-alone component, to a component within other software systems • consider the original goal was to build intelligence • later, the goal became problem solving systems – Now the goal is autonomous (agents) or semi- autonomous (robots) • software systems or systems that work with humans (data mining, decision support tools) • Machine learning has similarly shifted – Originally the concept was vague with no ideas of how to approach it • early symbolic approaches dealt with acquiring knowledge or building on top of already present knowledge • neural networks focused on training how to solve a given task – Today, we often look at learning as improving performance through training
- 2. Other Paradigm Shifts •Early AI was almost solely symbolic based – Early neural network research of the 1960s made no impact • In the 1980s, there was a shift away from symbolic to connectionism – But that shift was somewhat short-lived as neural network limitations demonstrated • Today, we see all kinds of approaches – Symbolic – knowledge-based • possibly using fuzzy logic – Symbolic – ontologies – Symbolic – probabilistic through networks (Bayesian, HMM) – Neural network – Genetic algorithms
- 3. AI Approaches inthe Future • Obviously, we can’t predict now what approaches will be invented in the next 5-10 years or how these new approaches will impact or replace current approaches – However, the following approaches are finding uses today and so should continue to be used • data mining on structured data • machine learning approaches – Bayesian and neural network, support vector machines • case based reasoning for planning, model-based reasoning systems • rules will continue to lie at the heart of most approaches (except neural networks) • mathematical modeling of various types will continue, particularly in vision and other perceptual areas – Interconnected (networks) software agents – AI as part of productivity software/tools
- 4. AI Research Forthe Short Term • Reinforcement learning – Applied to robotics • Semantic web – Semi-annotated web pages – Development of more intelligent agents • Speech recognition – Improvement in areas of accuracy, larger vocabulary, and speed (reducing amount of search) • Natural language understanding – Tying symbolic rule-based approaches with probabilistic approaches, especially for semantic understanding, discourse and pragmatic analysis
- 5. Continued • Social networks –Modeling and reasoning about the dynamics of social networks and communities including email analysis and web site analysis • Multi-agent coordination – How will multiple agents communicate, plan and reason together to solve problems such as disaster recovery and system monitoring (e.g., life support on a space station, power plant operations) • Bioinformatics algorithms – While many bioinformatics algorithms do not use AI, there is always room for more robust search algorithms
- 6. Some Predictions? • Next5-10 years – Work continues on semantic web, robotics/autonomous vehicles, NLP, SR, Vision • Within 10 years – part of the web is annotated for intelligent agent usage – modest intelligent agents are added to a lot of applications software – robotic caretakers reach fruition (but are too expensive for most) – SR reaches a sufficient level so that continuous speech in specific domains is solved – NLP in specific domains is solved – Reliable autonomous vehicles used in specialized cases (e.g., military)
- 7. And Beyond • Within20 years – robotic healthcare made regularly available – vision problem largely solved – autonomous vehicles available – intelligent agents part of most software – cognitive prosthetics – semantic web makes up a majority of web pages – computers regularly pass the Turing Test • Within 50 years – nano-technology combines with agent technology, people have intelligent machines running through their bodies! – humans are augmented with computer memory and processors – computers are inventing/creating useful artifacts and making decisions • Within 100 years (?) – true (strong) AI
- 8. Other Predictions • Wantto place a bet? These bets are available from www.longbets.org/bets – By 2020, wearable devices will be available that will use speech recognition to monitor and index conversations and can be used as supplemental memories – by Greg Webster (??) – By 2025, at least half of US citizens will have some form of technology embedded in their bodies for ID/tracking – Douglas Hewes (CEO Business Technologies) – By 2029, no computer will have passed the Turing test – by Ray Kurzweil (a well known entrepreneur and technologist) – By 2030, commercial passenger planes will fly pilotless – by Eric Schmidt (CEO Google) – By 2050, no machine intelligence will be self-aware – by Nova Spivack (CEO of Lucid Ventures) – By 2108, a sentient AI will exist as a corporation providing services as well as making its own financial and strategic decisions – by Jane Walter (??)
- 9. Wearable AI • Wearablecomputer hardware is becoming more prevalent in society – we want to enhance the hardware with software that can supply a variety of AI-like services • The approach is called humanistic intelligence (HI) – HI includes the human in the processing such that the human is not the instigator of the process but the beneficiary of the results of the process
- 10. HI Embodies 3Operational Modes • Constancy – the HI device is always operational (no sleep mode) with information always being projected (unlike say a wrist watch where you have to look at it) • Augmentation – the HI augments the human’s performance by doing tasks by itself and presenting the results to the human • Mediation – the HI encapsulates the human, that is, the human becomes part of the apparatus for instance by wearing special purpose glasses or headphones (but the HI does not enclose the human) – These systems should be unmonopolizing, unrestrictive, observable, controllable, attentive, communicative
- 11. HI Applications • Filteringout unwanted information and alerting – specialized glasses that hide advertisements or replace the content with meaningful information (e.g., billboards replaced with news) – blocking unwanted sounds such as loud noises with white noise – alerting a driver of an approaching siren – providing GPS directions on your glasses • Recording perceptions – if we can record a person’s perceptions, we might be able to play them back for other people – record a live performance – other examples include recording people performing an activity so that it can be repeated (by others) • record hand motions while the user plays piano • record foot motions while the user dances to capture choreography
- 12. Continued • Military applications –aiming missiles or making menu selections in an airplane so that the pilot doesn’t have to move his hands from the controls – some of this technology already exists – reconnaissance by tracking soldiers in the field, seeing what they are seeing • Minimizing distractions – using on-board computing to determine what a distraction might be to you and to prevent it from arising or blocking it out • Helping the disabled – HI hearing aids, HI glasses for filtering, internal HI for medication delivery, reminding and monitoring systems for the elderly
- 13. Beyond AI Wearables •As the figure below shows, these devices may be more intimately wound with the human body – We are currently attaching ID/GPS mechanisms to children and animals – Machine-based tattoos are currently being researched • What about underneath the skin? – Nano-technology – Hardware inside the human body (artificial hearts, prosthetic device interfaces, etc)
- 14. AI in Space/NASA •Planning/scheduling – Manned mission planning, conflict resolution for multiple missions – Multi-agent planning, distributed/shared scheduling, adaptive planning – Rover path planning – Telescope scheduling for observations – Deliberation vs. reactive control/planning – Plan recovery (failure handling) • Life support monitoring and control for safety – Simulation of life support systems – On-board diagnosis and repair • Science – Weather forecasting and warning, disaster assessment – Feature detection from autonomous probes – Other forms of visual recognition and discovery
- 15. Smart Environments • Sometimesreferred to as smart rooms • Components: collection of computer(s), sensors, networks, AI and other software, actuators (or robots) to control devices • Goal: the environment can modify itself based on user preferences or goals and safety concerns – smart building might monitor for break-ins, fire, flood, alert people to problems, control traffic (e.g. elevators) – smart house might alter A/C, adjust lighting, volume, perform household chores (starting/stopping the oven, turn on the dishwasher), determine when (or if) to run the sprinkler system for the lawn – smart restaurant might seat people automatically, have robot waiters, automatically order food stock as items are getting low (but not actually cook anything!)
- 16. Smart Windows • Oneof the more imminent forms of smart environments is the smart window – To help control indoor environments as an energy- saving device • The window contains several sheets of optical film, each of which is controlled by a roller that can roll the film up or down – there are six microcontrollers – presumably the system works by fuzzy logic although I could not find such details on how the controllers made decisions • An optical sensor allows the window to identify the current situation (too much light, too much heat, not enough heat) and respond by creating the needed level of translucency by sliding films up or down
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- 18. Automated Highways • Features –Provide guidance information for cooperative (autonomous) vehicles – Monitor and detect non-cooperative vehicles and obstacles – Plan optimum traffic flow • Architecture – Network of short-range hi-resolution radar sensors on elevated poles – Additional equipment in vehicles (transponders for instance for location and identification) – Sensors on the road for road conditions and on the vehicles for traction information – Sensors for other obstacles (e.g., animals) – Computer network – Roadway blocked off from sidewalk and pedestrian traffic
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- 22. Creating Human-level Intelligence •This was our original goal – Is it still the goal of AI? – Should this be the primary goal of AI? • What approaches are taking us in that direction? – Cyc? – Cog and other efforts from Brooks? – Semantic web and intelligent agents? • What do we need to improve this pursuit? – Study the brain? Study the mind? – Study symbolic approaches? Subsymbolic approaches? – Machine learning? • In spite of such pursuits, most AI is looking at smaller scale problems and solutions – And in many cases, we now are willing to embrace helper programs that work with human users
- 23. Social Concerns: Unemployment •According to economics experts, computer automation has created as many jobs as it has replaced – Automation has shifted the job skills from blue collar to white collar, thus many blue collar jobs have been eliminated (assembly line personnel, letter sorters, etc) • What about AI? – Does AI create as many jobs as it makes obsolete? • probably not, AI certainly requires programmers, knowledge engineers, etc, but once the system is created, there is no new job creation – Just what types of jobs might become obsolete because of AI? • secretarial positions because of intelligent agents? • experts (e.g., doctors, lawyers) because of expert systems? • teachers because of tutorial systems? • management because of decision support systems? • security (including police), armed forces, intelligence community?
- 24. Social Concerns: Liability •Who is to blame when an AI system goes wrong? – Imagine these scenarios: • autonomous vehicle causes multi-car pile-up on the highway • Japanese subway car does not stop correctly causing injuries • expert medical system offers wrong diagnosis • machine translation program incorrectly translating statements between diplomats leading to conflict or sanctions – We cannot place blame on the AI system itself – According to law, liability in the case of an AI system can be placed on all involved: • the user(s) for not using it correctly • the programmers/knowledge engineers • the people who supplied the knowledge (experts, data analysts, etc) • management and researchers involved – AI systems will probably require more thorough testing than normal software systems • at what point in the software process should we begin to trust the AI system?
- 25. Case Study: Therac-25 •Medical accelerator system to create high energy electron beams – used to destroy tumors, can convert the beam to x-ray photons for radiation treatments • Therac-25 is both hardware and software – earlier versions, Therac-6 and Therac-20, were primarily the hardware, with minimal software support – Therac-6 and -20 were produced by two companies, but Therac-25 was produced only be one of the two companies (AECL) , borrowing software routines from Therac-6 (and unknown to the quality assurance manager, from Therac-20) – 11 units sold (5 in US, 6 in Canada) in the early to mid 80s, during this time, 6 people were injured (several died) from radiation overdoses
- 26. The 6 ReportedAccidents • 1985: woman undergoing lumpectomy receives 15,000-20,000 rads – eventually she loses her breast due to over exposure to radiation, also loses ability to use arm and shoulder – treatment printout facility of Therac-25 was not operating during this session and therefore AECL cannot recreate the accident • 1985: patient treated for carcinoma of cervix – user interface error causes overexposure of 13– 17,000 rads, patient dies in 4 months of extremely virulent cancer, had she survived total hip replacement surgery would have been needed • 1985: treatment for erythema on right hip results in burning on hip, patient still alive with minor disability and scarring
- 27. Continued • 1986: patientreceives overdoes caused by software error, 16,500-25,000 rads, dies within 5 months • 1986: same facility & error, patient receives 25,000 rads & dies within 3 weeks • 1987: AECL had “fixed” all of the previously problems, new error of hardware coupled with user interface and operator error results in a patient, who was supposed to get 86 rads being given 8-10,000 rads, patient dies 3 months later – note: Therac-20 had hardware problems which would have resulted in the same errors from patients 4 and 5 above, but because the safety interlocks were in hardware, the error never arose during treatment to harm a patient
- 28. Causes of Therac-25Accidents • Therac-20 used hardware interlocks for controlling hardware settings and ensuring safe settings before beam was emitted • User interface was buggy • Instruction manual omitted malfunction code descriptions so that users would not know why a particular shut down had occurred • Hardware/software mismatch led to errors with turntable alignment • Software testing produced a software fault tree which seemed to have made up likelihoods for given errors (there was no justification for the values given)
- 29. Continued • In addition,the company was slow to respond to injuries, and often reported “we cannot recreate the error”, they also failed to report injuries to other users until forced to by the FDA • Investigators found that the company had “less than acceptable” software engineering practices • Lack of useful user feedback from the Therac- 25 system when it would shut down, failure reporting mechanism off-line during one of the accidents
- 30. Safety Needs inCritical Systems • It is becoming more and more important to apply proper software engineering methodologies to AI to ensure correctness – Especially true in critical systems (Therac-25, International Space Station), real-time systems (autonomous vehicles, subway system) • Some suggestions: – Increase the usage of formal specification languages (e.g., Z, VDM, Larch) – Add hazard analysis to requirements analysis – Formal verification should be coupled with formal specification • statistical testing, code/document inspection, automated theorem provers – Develop techniques for software development that encapsulate safety • formal specifications for component retrieval when using previously written classes to limit the search for useful/usable components • reasoning on externally visible system behavior, reasoning about system failures (this is currently being researched to be applied to life support systems on the International Space Station)
- 31. Social Concerns: Explanation •An early complaint of AI systems was their inability to explain their conclusions – Symbolic approaches (including fuzzy logic, rule based systems, case based reasoning, and others) permit the generation of explanations • depending on the approach, the explanation might be easy or difficult to generate • chains of logic are easy to capture and display – Neural network approaches have no capacity to explain • in fact, we have no idea what internal nodes represent – Bayesian /HMM approaches are limited to • probabilistic results (show probabilities to justify answer) • paths through an HMM
- 32. Continued • As AIresearchers have moved on to more mathematical approaches, they have lost the ability (or given up on the ability) to have the AI system explain itself – How important will it be for our AI system to explain itself? • is it important for speech recognition? • is it important for an intelligent agent? – here, the answer is probably yes, if the agent is performing a task for a person, the person may want to ask “why did you choose that?” • is it important for a diagnostic system? – extremely important • is it important for an autonomous vehicle? – possibly only for debugging purposes
- 33. Social Concerns: AIand Warfare • What are the ethics of fighting a war without risking our lives? – Consider that we can bomb from a distance without risk to troops – since this lessens our risk, does it somehow increase our decision to go to war? – How would AI impact warfare? • mobile robots instead of troops on the battlefield • predator drone aircraft for surveillance and bombing • smart weapons • better intelligence gathering – While these applications of AI give us an advantage, might they also influence our decision to go to war more easily? • On the other hand, can we trust our fighting to AI systems? – Could they kill innocent bystanders? – Should we trust an AI system’s intelligence report?
- 34. Social Concern: Security •In a similar vein, we are attempting to use AI more and more in the intelligence community – Assist with surveillance – Assist with data interpretation – Assist with planning • Will the public back AI-enhanced security approaches? • What happens if we come to rely on such approaches? – Are they robust enough? • Given the sheer amount of data that we must process for intelligence, AI approaches makes fiscal sense – How do we ensure that we do not have gaps in what such systems analyze – How do we ensure accuracy of AI-based conclusions? • In some ways, we might think of AI in security as a critical system, and AI in disaster planning as a real time system
- 35. Social Concerns: Privacy •This is primarily a result of data mining – We know there is a lot of data out there about us as individuals • what is the threat of data mining to our privacy? • will companies misuse the personal information that they might acquire? • We might extend our concern to include surveillance – why should AI be limited to surveillance on (hypothetical) enemies? – Speech recognition might be used to transcribe all telephone conversations – NLU might be used to intercept all emails and determine whether the content of a message is worth investigating • We are also seeing greater security mechanisms implemented at areas of national interests (airports, train stations, malls, sports arenas, monuments, etc) – cameras for instance – previously it was thought that people would not be hired to watch everyone, but computers could
- 36. What If StrongAI Becomes a Reality? • Machines to do our work for us leaves us with – more leisure time – the ability to focus on educational pursuits, research, art – computers could teach our young (is this good or bad?) – computers could be in charge of transportation thus reducing accidents, and possibly even saving us on fuel – computers may even be able to discover and create for us • cures to diseases, development of new power sources, better computers • On the negative side, this could also lead us toward – debauchery (with leisure time we might degrade to decadence) • consider ancient Romans had plenty of free time because of slavery – unemployment which itself could lead to economic disaster – if computers can manufacture for us anything we want, this can also lead to economic problems • We might become complacent and lazy and therefore not continue to do research or development
- 37. AI: The MoralDilemma • Researchers (scientists) have often faced the ethical dilemmas inherent with the product of their work – Assembly line: • Positive outcomes: increased production and led to economic boons • Negative outcomes: increased unemployment, dehumanized many processes, and led to increased pollution – Atomic research: • Positive outcomes: ended world war II and provided nuclear power, • Negative outcomes: led to the cold war and the constant threat of nuclear war, creates nuclear waste, and now we worry about WMDs – Many researchers refused to go along with the US government’s quest to research atomic power once they realized that the government wanted it for atomic bombs • They feared what might come of using the bombs • But did they have the foresight to see what other problems would arise (e.g., nuclear waste) or the side effect benefits (eventually, the arms race caused the collapse of the Soviet Union because of expense) – What side effects might AI surprise us with?
- 38. Long-term Technological Advances •If we extrapolate prior growth of technology, we might anticipate: – enormous bandwidth (terabit per second), secondary storage (petabyte) and memory capacities (terabyte) by 2030 • in essence, we could record all of our experiences electronically for our entirely lifetime and store them on computer, we can also download any experience across a network quickly • Where might this lead us? – Teleportation – combining network capabilities, virtual reality and AI – “Time travel” – being able to record our experiences, thoughts and personalities, in a form of agent representative, so that future generations can communicate with us – combining machine learning, agents, NLU – Immortality – the next step is to then upload these representatives into robotic bodies, while these will not be us, our personalities can live on, virtually forever
- 39. Ethical Stance ofCreating True AI • Today we use computers as tools – software is just part of the tool • AI is software – will we use it as a tool? • Does this make us slave masters? – ethically, should we create slaves? – if, at some point, we create strong AI, do we set it free? – what rights might an AI have? • would you permit your computer to go on strike? • would you care if your computer collects data on you and trades it for software or data from another computer? • can we ask our AI programs to create better AI programs and thus replace themselves with better versions? • What are the ethics of copying AI? – we will presumably be able to mass produce the AI software and distribute it, which amounts essentially to cloning • humans are mostly against human cloning, what about machine cloning?
- 40. End of theWorld Scenario? • When most people think of AI, they think of – AI run amok • Terminator, Matrix, etc (anyone remember Colossus: The Forbin Project?) – Would an AI system with a will of its own (whether this is self-awareness or just goal-oriented) want to take over mankind or kill us all? • how plausible are these scenarios? • It might be equally likely that an AI that has a will of its own would just refuse to work for us – might AI decide that our problems/questions are not worthy of its time? – might AI decide to work on its own problems? • Can we control AI to avoid these problems? – Asimov’s 3 laws of robotics are fiction, can we make them reality? – How do we motivate an AI? How do we reward it?