Unit 1: Outcomes of this program

  • It not about smarter machines, it’s about smarter organizations…
    • Collective Intelligence: Groups of individuals working together, in ways that seem intelligent…
    • In tech think of Google, or Wikipedia, Technology enabled collective intelligence
  • Question: How can people and computers be connected, so that collectively they act more intelligently than any person or group has done before.
    • Connecting people together in new ways so they can act more intelligent as a group
    • Connecting people to computers that have more Artificial intelligence
  • After the course, I will know…
    • more about AI in business
    • opportunities for AI in busienss
    • ideas and concrete plan as to how MY org can use AI
    • Will understand generally about AI in the world.
  • If you are afraid of AI, the hope is that you will not be at the end.

Unit 2: An overview of AI: definition, history, and current state

  • Defining AI
    • The term “artificial intelligence” is not easy to define. The word “artificial” is more straightforward, meaning something that doesn’t occur naturally. By contrast, “intelligence” has been defined in many ways. One good definition, by the psychologist Howard Gardner, focuses on problem-solving: “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings” (Gardner 1983). Informally, people sometimes use the term “artificial intelligence” to mean only those activities that are hard for computers to do (like understanding English) as opposed to simpler activities computers routinely do today (like accounting). 
    • An important distinction in the field of AI is between “narrow AI” and “general AI”. Narrow AI is defined as “a machine-based system designed to address a specific problem (such as playing Go or chess)” (Kiron 2017). By contrast, general AI refers to machines with the ability to solve many different types of problems on their own, like humans can. To date, all applications of AI are examples of narrow AI. Although general AI is currently a hot research topic, it is still likely decades away from true realization.
    • For the purposes of this program, 
      • Professor Malone’s intuitive definition of AI is that it is: “AI is…machines acting in ways that seem intelligent”. 
      • Professor Patrick Winston’s more formal definition is: AI is about the architectures that deploy methods enabled by constraints exposed by representations that support models of thinking, perception, and action. And of course, it’s not just about doing, it’s also about learning to do.
  • Professor Patrick Winston on the history of AI
    • Professor Malone’s intuitive definition of AI is that it is “machines acting in ways that seem intelligent”.
    • Computation isn’t just changing somethings, it’s changing everything
    • “Founders” in the field of artificial intelligence
      • Alan Turing, 1950 paper.. “Turing test”.. 5 min computer of person… trying to deal with objections of humans, vs just a test of intelligence.  – We can do this…
      • Marvin Minsky and the suitcase of words for AI… “It’s a suitcase term.”  Minsky’s paper “Steps towards artificial intelligence 1961” – What to do to get there…
    • AI’s 1st Wave… 1960’s
      • James Slaegal, MIT work in symbolic expressions. All about Problem Reduction, or breaking problems in to simpler, and simpler problems…
      • If you get the representation right, you’re also most done.
      • Professor Patrick Winston “AI is about models of thiking, preception, and action…”
        • model … behaves like the real things…
          • representations .. support models… set of conventions for describing situations.
            • representations expose constraints
              • methods to deals with constraints
                • Architectures for the methods.
    • AI’s 2nd wave – mid 1970
      • Ed Shirtliffe, Stanford , MYCIN – Diagnose infectious blood disease, Rules based expert systems 
    • More History…
      • Summer of 1956 researchers got together at Dartmouth to start laying foundation in researching and developing artificial intelligent human being.
        1. Design goals of artificial intelligence
          1. Reasoning
          2. Knowledge Representation  – John McCarty invented Lisp, language to helpd define knowledge
          3. Planning (including navigation)
          4. Natural language processing
          5. Perception. How doe we feel, hear, smell, things in the world
          6. Generalized Intelligence. (including emotional intelligence, creativity, moral reasoning, intuition, etc.)
      • Many boom/bust cycles – AI winters
        • 1960’s  – Translation – 60 Russian sentences in to English. 701 translator – punch card demo – no semantics…context idioms…meaning was lost.. Funding killed for a decade
        • 1970’s – Micro-worlds understanding language in smaller contexts. Eliza – talk therapy…
          • Pick up a blue block
          • put blue bock on top of red block…
        • 1980’s – expert systems – 
      • The breakthrough…
        • Deep learning. McCullough and Pitts, 1940’s modeling computer on the way the brain works… 
        • Effectively let’s write code that mirrors the neuron to neuron system in the brain , using weights
        • 2012 – Andrew Ing used 10m YouTube videos Google brought data and scale… It found cats, they recognized 16% of other objects in the stills. They did not have to tell it what to look for. it found it. THis is a Data Up approach
          • why7 now…
        • How does it work
          • Define the number of layers, this is the deep… then give each layers some neurons… links are build between the neurons in each layers strong/week… the system will then determine what’s in the data… setting the weights on each connection,
            • Artificial intelligence -> Machine Learning – >deep learning
        • AI can make humans better…
  • The future of intelligence
    • 15 to 25 years to reach general AI… has been the same for the last twenty years.
    • We do not understand enough about ourselves…
    • Collective intelligence will most likely lead the way. Human/human computer/human, computer/computer
    • The theory of multiple intelligences was developed in 1983 by Dr. Howard Gardner, professor of education at Harvard University. It suggests that the traditional notion of intelligence, based on I.Q. testing, is far too limited. Instead, Dr. Gardner proposes eight different intelligences to account for a broader range of human potential in children and adults. These intelligences are:
      • Linguistic intelligence (“word smart”)
      • Logical-mathematical intelligence (“number/reasoning smart”)
      • Spatial intelligence (“picture smart”)
      • Bodily-Kinesthetic intelligence (“body smart”)
      • Musical intelligence (“music smart”)
      • Interpersonal intelligence (“people smart”)
      • Intrapersonal intelligence (“self smart”)
      • Naturalist intelligence (“nature smart”)

Unit 3: Combining people and computers [± 1 hour 20 minutes]

  • All real uses of AI involve people and computers, people are:
    • Creative software
    • selecting applications
    • fixing problerms
    • actions only a human can do
  • We are not just trying to optimize computer systems, they are human-computer systems.
  • Questions:
    • What tasks should computer do, vs people?
      • machines can remember hug amounts of info
      • people interact flexible with other people
      • We should not think about how computers will replace people, but how we can do things better together, and new things together.
      • Think Google man made content, indexed and cataloged by Google’s algorithms… Lost some reference librarians, but gained many jobs in search and advertising. and Wikipedia bots checking content as it’s actively updated… CSAIL cyber security systems machine can find event, people can reason the event… find 3x more events..
      • Four roles computers can play:
          • People and computer working together: combination was more accurate and more robust.
          • Crowdforge – machine and people working together, with the machine having oversight and coordination
          • Cogito… listening to customer calls to assess tone and mood, to inform rep.
    • How can this system improve over time?
      • Ever evolving systems, learning from experince top get better and better over time.
      • Cyber human learning loop
        • Humans get better
        • programmers improve machines
        • Machines learn from experience, with various forms of machine learning
      • Strategic organization need to:  think carefully about how we divide the tasks betwen humans and computers, and constatnly learn from experience.

Unit 4: How to gain strategic advantage [± 2 hours 4 minutes]

  • Professor Malone begins by describing Michael Porter’s framework for understanding how organizations can gain an advantage over their competitors. Porter identified three generic strategies that companies can use:
    • Cost leadership (being the low-cost producer)
      • improving operations, loss prevention, using robots, 
    • Differentiation (being unique on dimensions that customers value, such as quality)
      • incorporating new features that could not be identified before. Look at Google assistant Watson and patient data.
    • Focus (tailoring products to a narrow segment of customers)
      • Uniques need of customers and individuals. Think recommendation from Netflix and Amazon.
  • In general, your strategy for using AI should be consistent with your organization‘s overall strategic approach. However, in some cases, AI may make an entirely new strategy feasible for your organization.

Module Artifacts:

15 key moments in the story of artificial intelligence.pdf2017-report.pdfMIT AI M1 U1 Video Transcript.pdfMIT AI M1 U2 Casebook Video 1 Transcript.pdfMIT AI M1 U2 Casebook Video 2 Transcript.pdfMIT AI M1 U2 Casebook Video 3 Transcript.pdfMIT AI M1 U2 Casebook.pdfMIT AI M1 U3 Video 1 Transcript.pdfMIT AI M1 U3 Video 2 Transcript.pdfMIT AI M1 U4 Video Transcript.pdfMIT AI _ M1U1 Tom Malone.mp4Preparing for the Future of Artificial Intelligence.pdfWhat Managers Need to Know About Artificial Intelligence.pdfWhat can AI do right now.pdfai100report10032016fnl_singles.pdf