17: Artificial Intelligence & Machine Learning

Basic definitions #

  • Artificial Intelligence (AI): tries to design computer models that accomplish the same cognitive tasks that humans do
  • Machine Learning: a subset of AI that allows computers to “learn” (i.e., progressively improve performance on a specific task) by creating new algorithms to produce a desired output based on structured (or unstructured) data that is provided
  • Deep Learning: a subset of Machine Learning involving numerous layers of algorithms
    • Computer does not need to be provided with structured data
  • Neural Networks: Networks of algorithms that are similar to the neural networks present in the human brain

Artificial Intelligence Formulations #

There are a number of different artificial intelligence formulations

  1. Strong AI: General purpose AI
    • Machines that possess artificial general intelligence (AGI)
    • Would be just as smart as humans across the board with the ability to understand and learn any task that a human can
  2. Applied AI: AGI isn’t going to be created any time soon, but machine learning has made it possible for machines to learn how to master complex tasks (expert systems), including
    • Playing the ancient Chinese board game Go
    • Identifying human faces
    • Translating text into practically every language
    • Driving cars
  3. Computer replication: Understand how the mind works and replicate its functions in machine or organic form

Machine Learning #

  • Problem: How can a computer learn to distinguish between pictures of dogs and cats?
  • Approach: You label pictures of dogs and cats with specific defining characteristics (e.g., length of ear, color of nose), then feed this structured data through the computer

Deep Learning #

  • You feed the computer pictures labeled as dogs or cats without additional structured data
  • Data is sent through different layers of the artificial neural network corresponding to different layers of abstraction, from low level (e.g., does this part of picture contain a brown spot?) to more complex, e.g., (is there a nose in this part of the picture?)

Supervised, Unsupervised, and Deep Reinforcement Learning #

  1. Supervised learning: network receives explicit feedback on how successful it is
  2. Unsupervised learning: network does not receive explicit feedback; instead it learns to detect patterns in data
  3. Reinforcement learning: distinct from both of the above in that…
    • It does depend upon a feedback signal
    • However, the feedback signal does not tell the network exactly what it has done wrong; instead, the network is driven by a reward signal
      • The job of the network is to maximize the reward, but it is not told how to do that
      • It has to work out for itself which outputs are most profitable
    • Ex: Atari breakout

AlphaGo #


In 2016 and 2017, AlphaGo program created by Google’s Deep Mind research group beat the world’s leading human experts at the game of Go

  • These victories were widely recognized as historic achievements for AI
  • Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent
    • It is one of world’s most complex games
    • Chess has $10^{123}$ possible moves; Go has $10^{360}$
  • AlphaGo used a mixture of supervised learning and reinforcement learning
    • Was initially trained on a database of 30 million moves from an online server using supervised learning – received explicit feedback on how successful it was
    • Once AlphaGo had achieved a relatively high level of playing strength, training shifted to reinforcement learning
      • In reinforcement learning, network will not increase its reward simply by repeating what has worked in the past
      • It needs to engage in trial and error to discover new reward-generating strategies
      • Reinforcement learning was achieved by getting the network to play games of Go against former versions of itself
  • New version, AlphaGo Zero, incorporated zero supervised learning
    • After three days (and 4.9 million games played against itself), it was able to beat the version of AlphaGo that had defeated the leading human expert in 2016 – 100 games to 0!
    • Within 40 days, it was able to beat all existing versions of AlphaGo

Applications of AI #

Healthcare #

  • Diagnosis of disorders, e.g., IBM Watson
    • Computers found to be as good or better than doctors at detecting tiny lung cancers on CT scans
    • Potential hazard: A radiologist who misreads a scan may harm one patient, but a flawed A.I. system in widespread use could injure many
  • Determining optimal treatment, including
    • Type and dosage of drugs
    • Best diet for individual, e.g., to avoid glucose spikes after eating
      • This may vary, depending on patient’s unique gut microbiome
  • Precision surgery without human artifacts like handshaking
    • Study comparing computer-controlled robots with human surgeons in performing intestinal surgery on a pig found that the robot sutures were much better—more precise and uniform with fewer chances for breakage, leakage, and infection (Shademan, Decker, Opfermann et al., 2016)

Brain Computer Interface #

Brain Computer Interface (BCI) or Brain Machine Interface (BMI): “neural prosthetics”

  • Computer chip is implanted in motor cortex and communicates directly with external device
  • Allows animal (or person) to directly control a robotic arm with their thoughts

A cap with electrodes can now be used instead of implants, but an extensive calibration process is required

Direct brain-to-brain communication in humans is also now possible

  • Two research participants are are positioned in two different buildings on campus
    • The sender, left, thinks about firing a cannon at various points throughout a computer game
    • That signal is sent over the Web directly to the brain of the receiver, right, whose hand hits a touchpad to fire the cannon (Rao, Stucco, Ryan et al., 2014)

Psychotherapy #

Diagnosis/identification of psychological disorders

  • AI system that analyzed Facebook posts of consenting patients in an emergency department was able to generate predictions of depression risk that were as accurate as standard depression screening tests (Reece & Danforth, 2017)
    • Indicators included references to sadness, loneliness, hostility, rumination, and increased self-reference, e.g., words like “alone,” “ugh,” “tears,” and higher frequency of use of “I” and “me”
    • Length and timing of posts were also considered (Eichstaedt, Smith, Merchant et al., 2018)
  • AI has also been used to analyze Instagram photos to successfully screen for depression
    • Photos posted by depressed individuals tended to be bluer, darker, and grayer
    • The more comments Instagram posts received, the more likely they were posted by depressed participants, but the opposite was true for likes received
    • Depressed participants were more likely to post photos with faces, but had a lower average face count per photo than healthy participants

    The screening models created from the data were able to outperform general practitioners in correctly diagnosing depression without the assistance of assessment instruments

A.I.-driven voice analysis

  • Researchers are currently working on developing voice analysis programs that can help identify psychological disorders
    • In depression, speech is generally more monotone, with reduced pitch range and lower volume; there may be more pauses
    • In anxiety, speech tends to be faster, and there may be evidence of difficulty breathing
    • Programs may also be used to predict other mental illnesses like schizophrenia and PTSD
  • Use of deep-learning algorithms can uncover patterns that might not be evident even to trained experts
    • “The technology that we’re using now can extract features that that even the human ear can’t pick up on.”
  • Potential problems:
    • It can be difficult to know why your vocal levels fluctuate, e.g., you may just be trying to speak quietly
    • Issue of bias: need to ensure that programs work for all patients, regardless of age, gender, ethnicity, etc.
    • Deep learning algorithms work in ways that even the developers themselves can’t fully explain, that is, knowing which features are being used to make the predictions

Treatment of psychological disorders #

  • Apps that can administer Cognitive Behavioral Therapy for disorders like depression or social anxiety, e.g., Woebot
  • Virtual therapists, e.g., Ellie
  • These programs may analyze tone of voice, breathing pattern, smartphone keystrokes and communication, and/or physical movements in making diagnoses and generating responses
  • Pros:
    • Easy accessibility and affordability
    • Research has indicated that people would rather share their innermost secrets with an avatar than a human being
  • Cons:
    • Can’t really replace human empathy
    • Adherence to treatment may be poor

Transportation #

Self-driving cars

  • 2018 was the target date proposed in 2015 both by Elon Musk of Tesla and by Google for introduction of self-driving cars
  • But a series of widely publicized crashes, some fatal, have delayed release

Are self-driving cars really a viable option?

  • The great successes of deep learning have all been in relatively circumscribed domains, including chess and Go, and even things like image recognition, which primarily involves identifying patterns in a data set, then projecting those patterns onto new exemplars
  • Some believe that self-driving cars (and other autonomous vehicles, like drones) need more than sensitivity to patterns and the ability to learn from experience
    • They need to be able to deal with the unexpected – completely unpredictable behavior from other drivers, pedestrians, cyclist, and even wild animals
  • Also, human drivers are constantly exploiting their knowledge of how physical objects move and behave (folk physics), as well as their knowledge of other drivers and road-users (mindreading)
    • Perhaps a key challenge for designers of self-driving cars is how to equip their vehicles with this kind of general knowledge
  • Pros:
    • Less human error = more lives saved
    • Accessible to those who cannot drive
    • Can engage in other activities during commute
  • Cons:
    • Criminal hacking or system glitches
    • Loss of jobs
    • High initial cost
    • Fewer people using public transportation

Moral issues #

  • Cars will need to make “moral” decisions in unavoidable accidents
  • Which person to sacrifice, the pedestrian or the driver?

Robots #

  • Social robots
  • Home robots: cooking, cleaning, fetching…
    • Moley learns to cook by using data from the motion-capturing gloves and wristbands of a master chef

Other applications #

  • Language processing: Use of natural language processing of speech to synthesize notes in professional settings
  • Advertising: Tracking customer behavior to target them with personalized promotions
  • Customer service: Help lines; providing information to consumers
  • Finance and economics:
    • Record keeping
    • Fraud detection
    • Optimizing profits in online trading
    • Predicting market supply and demand

Risks and Dangers of AI #

  • Deliberate programming of AI to be hostile, e.g., by terrorist group
  • Sci-fi scenario: If human behavior contradicts one of AI’s preprogrammed goals, AI could turn malicious
    • In 2017, two Facebook computers started communicating with each other in a language they had developed on their own
  • Simulation of government leader’s image and voice issuing unauthorized orders, e.g., military action
  • Generation of fake emails, phone calls, video chats
  • Loss of privacy
  • Loss of jobs
    • Some AI experts predict that AI will replace or eliminate 40% of jobs within 15 years
    • Greatest impact will be on jobs involving tasks that are repetitive and can be automated

  • Other AI-related risk: Development of computer technology has meant an increase in the amount of time we spend looking at computer screens, and that may be harmful
    • Recent study published in JAMA Pediatrics found that after controlling for age, gender and income, 3-5 year old children with higher use of screen-based media
      • Had lower measures of structural integrity and myelination
      • Scored lower on cognitive tests (Hutton, Dudley, Horowitz-Kraus et al., 2020)
    • Similarly, in older adults, increased television viewing was found to be correlated with cognitive impairment and poor verbal memory (Fancourt and Steptoe, 2019)
    • In fruit flies, daily blue-light exposure (such as is used in computer screens) causes brain neurodegeneration, as well as shortening of lifespan (Nash, Chow, Law et al., 2019)

American Academy of Pediatrics recommendations:

  • Children under 18 months should avert their eyes from TV and screen media at all times
  • For children 2 to 5, screen time should be limited to no more than 1 hour per day

Issue of biases #

  • Back in 2015, software engineer Jacky AlcinĂ© pointed out that the image recognition algorithms in Google Photos were classifying his African American friends as “gorillas”
    • Google said it was “appalled” at the mistake, apologized to AlcinĂ©, and promised to fix the problem
    • However, three years later, Google still had not really fixed anything – the company simply blocked its image recognition algorithms from identifying gorillas altogether
      • Baboons, gibbons, and marmosets were all correctly identified, but gorillas and chimpanzees were not
  • Try typing things like “why Black/Latinx/Asian women/men/boys/girls …” on Yahoo or Bing
    • You’ll get really biased, racist and discriminatory auto-suggestions
    • These are now blocked on Google, but not on other search engines

AI and jobs #

  • On the other hand, AI has also created new jobs, e.g., AI designer, software engineer, cybersecurity developer, machine relations manager
  • Unfortunately though, people whose jobs are taken away may not necessarily be those who get new jobs created by AI development

Jobs that Are Hard to Replace #

  • Emotionally demanding jobs
    • Therapist
      • Depends on individual (some veterans like talking to AI)
    • Taking care of babies/children
    • Human Resources
    • Politician
  • Creative jobs
    • Writer
    • Software/graphic designer

Things We Can Do that AI Cannot Do Well #

  • Feel or show empathy
  • Have insights
    • As Anthony Goldbloom puts it, machines cannot “connect seemingly disparate threads to solve problems they have never seen before”
    • Machines can’t tackle novel situations
    • Need to learn from large volumes of past data
      • Percy Spencer was working on radar during World War II when he noticed a magnetron (used to generate radio signals) melting his chocolate bar
      • This led to the discovery of the Microwave oven!
  • Make plans for the distant future
    • Humans can plan their lives years in advance
    • Robots tend to focus only on completing the immediate task at hand
  • Be conscious (?)
    • Whether machines can have consciousness depends on the definition of consciousness
    • No machine today meets all the criteria that we may give to consciousness and that humans have