New Computing In 2019 and Beyond - Opportunities,

Challenges, and ThreatsFromm Institute

Fall 2019 - lecture 2 Bebo White - bebo.white@gmail.com

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questions (1/2)

1. You said that Net is not equal Web, so why a “Web Cam?”

2. It appears that sensors are the key component to IOT - correct?

3. Will you explain 5G to us? (next page)

4. If my refrigerator generates data, is that data saved somewhere? (I hope not)

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•2G ->3G: mobile web •3G->4G (LTE): HD video on smartphone •4G->5G: iot plus ???

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questions (2/2)

5. I first heard about IOT in connection with the revelation that they all can be hacked. I feel all connected things have some danger - please comment

6. URL for cryptocurrency/blockchain course - http://bit.ly/2M7xPgs

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calendar

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-Stephen Hawking

“Success in creating effective AI, could be the biggest event in the history of our civilization. Or

the worst. We just don’t know. So we cannot know if we will be infinitely helped by AI, or ignored by it and side-lined, or conceivably

destroyed by it. Unless we learn how to prepare for, and avoid, the potential risks, AI could be the

worst event in the history of our civilization. It brings dangers, like powerful autonomous

weapons, or new ways for the few to oppress the many. It could bring great disruption to our

economy”

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who is civilized here?

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this is not a joke

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2001: A Space Odyssey (1968)

The HAL 9000 - maybe the “gold standard”

what’s going on here?

• HAL is

• perceiving a personal threat (survival)

• determining a course of action

• executing an action

• is that intelligence? if so,

• was it taught (via human programming)?

• was it learned?

• is it all an illusion, a trick, slight-of-hand?

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mechanical turk - wolfgang von kempelen (1770)

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in full disclosure

chess is “a poster child” for AI

the turing test (1950)• “The Imitation Game” - “can a machine exhibit

intelligent behavior equivalent to, or indistinguishable from, that of a human?”

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what is artificial intelligence (AI)?

• AI is a programmed ability to process information (DARPA)

• a machine (computer?) with the ability to perform cognitive functions such as perceiving, learning, reasoning, and problem solving can be said to have artificial intelligence

• AI exists when a machine (computer?) has cognitive ability

• the common benchmark for AI is the human level concerning reasoning, speech, and vision (e.g., HAL)

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-Freeman Dyson

“the search for extraterrestrial intelligence is wrong…what we are really looking for is extraterrestrial

technology because we can see it. Intelligence and technology are two different things…you could have a mind and intelligence that has no

technology at all. ”

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with a nod to andrew fraknoi

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relationships of selected ai definitions

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brief history of artificial intelligence (as ai)

• 1956 - the Dartmouth Summer Research Project on Artificial Intelligence coins the name

• 1965 - Joseph Weizenbaum at MIT creates Eliza

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brief history of artificial intelligence (as ai)(2/2)

• 1975 - Meta-Dendral, a Stanford computer program for chemical analysis is published in a journal

• 1987 - a Mercedes van drives itself 20 km on a German highway

• 1997 - Deep Blue defeats Garry Kasparov

• 2004 - the DARPA Grand Challenge for robot cars in the Mojave Desert

• 2011 - Apple introduces Siri

• 2014 - Amazon introduces Alexa - inspired by Star Trek

• 2016 AlphaGo defeats Go world champion

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top myths about ai(1/2)

• myth: superintelligence by 2100 is inevitable; superintelligence by 2100 is impossible; fact: it may happen in decades, centuries or never

• myth: only Luddites worry about AI; fact: many top AI researchers are concerned

• mythical worry: AI turning evil; AI turning conscious; actual worry: AI turning competent with goals misaligned with ours

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top myths about ai(2/2)

• myth: robots are the main concern; fact: misaligned intelligence is the main concern; it needs no body, only a network connection

• myth: AI can’t control humans; fact: intelligence enables control: we control dogs by being smarter(?)

• myth: machines can’t have goals; fact: a heat-seeking missile has a goal

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ai levels

• narrow AI - when the machine can perform a specific task better than a human

• general AI - when a machine can perform any intellectual task with the same accuracy level as a human would

• strong AI - when a machine can beat humans in many tasks; “the appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds?”(John Searle)

• where are we now? Examples?

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John Searle (1980)(1/2)

• philosopher

• “Minds, Brains, and Programs”

• “Chinese Room Argument” (kind of like the Turing Test) contends that a computer executing a program cannot have a “mind,” “understanding” or “consciousness” no matter how human-like it behaves; it does not literally understand Chinese, but simulates the ability to understand Chinese

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Ex Machina (2014) a programmer is invited to administer the Turing Test

John Searle (1980)(2/2)

• ascribes the following positions to advocates of strong AI

• AI systems can be used to explain the mind

• the study of the brain is irrelevant to the study of the mind

• the Turing test is adequate for establishing the existence of mental states

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types of ai

• AI can be divided into 3 subfields

• artificial intelligence

• machine learning

• deep learning

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machine learning (ml)

• the art of study of algorithms that learn from examples and experiences

• based upon the idea that there exists patterns in a dataset that can be identified and used to make future predictions; do humans do this?

• the difference from strict programming is that the machine learns on its own from data to find rules rather than having them dictated in an algorithm/program code

• programmers provide a set of examples and the computer learns patterns from the data

• ML = “programming with data”

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ml is a “suitcase word” - marvin minsky

• words that carry a variety of meanings - learning is a powerful suitcase word

• learning refers to many different types of experience

• learning to write computer code is a very different experience from learning your way in San Francisco

• machines do not learn like humans; they do not learn (as humans do) to play games like chess, Go, or Jeopardy

• “machine learning” misleads people about how well machines are doing at tasks that humans can do

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ml flow - the path from data to knowledge

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Data target data

preprocessed data

transformed data

patterns/ models knowledge

selection

preprocessing

transformation

data mining

interpretation/ evaluation

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Again, think about human learning pedagogies

deep learning

• deep learning is a subfield of ML;

• it does not mean that the machine learns more in-depth knowledge, it means that the machine uses different layers to learn from data

• the depth of a model is represented by the number of layers in the model

• the learning phase is done through a neural network

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neural network

• a set of algorithms, modeled loosely after an animal/human(?) brain

• a type of model that can be trained to recognize patterns

• composed of layers, including I/O layers, and at least one hidden layer

• neurons in each layer learn increasingly abstract representations of the data

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-Andy Clark, University of Edinburgh

“Our brains have been tailored by millions of years of evolution to help us deal with the kinds of objects and structure that we’re

likely to encounter in the world. AI systems, however, start pretty much from scratch.

There’s also an architectural difference. A lot of deep-learning systems, which use many layers of artificial neurons to progressively extract features from raw data, do not work in a top-down, prediction-driven way, unlike the brain. They work in a more feed-forward

way.”

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neural networks learn from data

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(ref: DARPA)

example - image recognition (supervised algorithm)

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example - imagenet

• an image database organized according to the WordNet hierarchy (currently only the nouns)

• each node of the hierarchy is depicted by hundreds and thousands of images

• current average is over 500 images per node

• http://www.image-net.org/about-overview

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what are these?

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how long before this technology is outdated?

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-Dr. Arati Prabhakar, former DARPA Director

“When we look at what’s happening with AI, we see something that is very

powerful, but we also see a technology that is still quite

fundamentally limited…the problem is that when it’s wrong, it’s wrong in ways that no human would ever be

wrong”

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Context can be a problem

google tensorflow

• an end-to-end platform for building and deploying ML models

• used by

• Airbnb - to classify images and detect objects at scale

• GE Healthcare - training a neural network to identify specific anatomy during MRIs

• PayPal - to detect fraud patterns

• Rainforest Connection - to detect illegal logging

• etc., etc.

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(full disclosure)

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